The output gap indicating the difference between the actual and potential levels of output is a critical factor for estimating the inflationary pressures in an economy. If the main target of a central bank is ensuring and maintaining the price stability, estimating the output gap with a minimum error is crucial for the efficiency of the monetary policy. In this study, we estimated the output gap in Turkey for the 2002-2014 period by using four different methods. Two of these estimation methods are purely statistical (Linear Trend and Hodrick-Presscot (HP) Filtering) while the others are integrated with the relations suggested by the economic theory (multivariate structural model and structural autoregressive (SVAR) model). By using empirical decision criteria common in the literature, we conclude that SVAR model produces the most reliable output gap estimates to explain inflationary pressures in Turkey. However, we also found that the Hodrick-Presscot filtering method is the second best methodology in the output gap estimation process.
This paper provides a review of the empirical macroeconomic model (EMMA) built for forecasting purposes at the Finnish Labour Institute for Economic Research. The model is quite small, consisting of 71 endogenous and 70 exogenous variables. The number of behavioural equations is 15. The basis of the model is Keynesian, although the model has some novel properties. They are the treatment of the supply side and prices that follow the routes of the neoclassical synthesis. The parameters of the model are estimated from quarterly data that cover the years 1990–2005. The model also contains a Kalman-filtered variable to control the deep recession in Finland at the beginning of the ’90s. This special feature brings the model closer to the new calibrated models.
To analyze the factors affecting the price volatility of stocks, microeconomic and macroeco-nomic elements must be considered. This paper selects elements that are appropriate with the daily data of stock prices to build the GARCH family models. External variables such as global oil prices, consumer price index, short interest rates and the exchange rate between the United States Dollar and the Euro are examined. The GARCH models are developed in order to analyze and forecast the stock price of the companies in the DAX 30, which is Germany’s most important stock exchange barometer. The volatility of the residual of the mean function is the important key point in the GARCH approach. This financial application can be extend-ed to analyze other specific shares or stock indexes in any stock market in the world. There-fore, it is necessary to understand the operating procedures of their pricing for risk manage-ment, profitability strategies, cost minimization and, in addition, to construct the optimal port-folio depending on investor’s preferences.
An Application of Tobit Regression on Socio Economic Indicators in Gujaratijtsrd
The use of factual estimation frameworks to consider human behavior in a social environment is known as social insights. In this study researcher examined. Socio Economics indicators like Education, Health and Employment in Gujarat he also used Tobit Regression as a statistical tool. It will be found that the most of the Sub Indicators are positively impact on Tobit Regression model. Dr. Mahesh Vaghela "An Application of Tobit Regression on Socio Economic Indicators in Gujarat" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46309.pdf Paper URL : https://www.ijtsrd.com/mathemetics/statistics/46309/an-application-of-tobit-regression-on-socio-economic-indicators-in-gujarat/dr-mahesh-vaghela
The purpose of the paper is to assess the validity of two interpretations which have been used in the description of the relationship between employment growth and economic activity in Finland during the 1990s. According to the New Era view the Finnish economy has moved into a new era which, as a result of a faster-than-before rate of labour productivity growth, is characterized by "jobless growth". According to the Cyclical Rebound view no change in the rate of trend productivity growth has taken place. The productivity-led growth, which after the very deep depression characterized the recovery of the economy, only reflected a normal cyclical rebound. The main result of my investigation is as follows. Neither the New Era view nor the Cyclical Rebound view provides a telling interpretation about the developments of productivity and the relationship between output and employment growth in the 1990s. Characterizing the years of the recovery as reflecting a New Era which is associated with an increase in the rate of longrun productivity growth is misleading, because that kind of change has not taken place. On the other hand, the movements of productivity are hard to reconcile with the Cyclical Rebound view because the years from 1992 to1994, especially, were exceptional. During the period movements in productivity were not consistent with a pro-cyclical pattern, and, what is important, the productivity trend shifted upwards. However, the shift was not associated with an acceleration in the rate of trend productivity growth. The upward shift was caused by a sequence of positive technology shocks, which were identified by using a structural VAR model. The identifying restriction was rationalized by utilizing a new Keynesian dynamic general equilibrium model. The positive technology shocks which dominated the developments of aggregate productivity during the period from 1992 to 1994 mainly reflect micro-structural changes like business restructuring and labour reallocation in manufacturing
Efeitos de crescimento das reformas estruturais na Europa do Sul - 511: O cas...Cláudio Carneiro
Este trabalho desenvolve um modelo de crescimento semi-endógena para analisar os efeitos intertemporais das reformas estruturais nos países do sul da Europa (Itália, Espanha, Portugal e Grécia). O modelo segue o paradigma variedade de produtos em um ambiente semi-endógena, e inclui uma desagregação do trabalho em grupos diferentes de habilidade. Nós usamos um conjunto abrangente de indicadores estruturais, a fim de calibrar o modelo de relações macroeconômicas importantes e os níveis de produtividade e do emprego. Nossos resultados mostram que as reformas estruturais produzir ganhos econômicos significativos a médio e longo prazo. Os resultados apontam para a importância das reformas dos mercados de produtos e de reformas educacionais e fiscais do mercado de trabalho como as áreas mais promissoras de intervenções de política estrutural. Este documento também defende a colocar mais ênfase na política de educação que é fundamental na melhoria da força de trabalho, especialmente naqueles países onde a percentagem de trabalho pouco qualificado está entre as mais altas na área do euro.
This paper provides a review of the empirical macroeconomic model (EMMA) built for forecasting purposes at the Finnish Labour Institute for Economic Research. The model is quite small, consisting of 71 endogenous and 70 exogenous variables. The number of behavioural equations is 15. The basis of the model is Keynesian, although the model has some novel properties. They are the treatment of the supply side and prices that follow the routes of the neoclassical synthesis. The parameters of the model are estimated from quarterly data that cover the years 1990–2005. The model also contains a Kalman-filtered variable to control the deep recession in Finland at the beginning of the ’90s. This special feature brings the model closer to the new calibrated models.
To analyze the factors affecting the price volatility of stocks, microeconomic and macroeco-nomic elements must be considered. This paper selects elements that are appropriate with the daily data of stock prices to build the GARCH family models. External variables such as global oil prices, consumer price index, short interest rates and the exchange rate between the United States Dollar and the Euro are examined. The GARCH models are developed in order to analyze and forecast the stock price of the companies in the DAX 30, which is Germany’s most important stock exchange barometer. The volatility of the residual of the mean function is the important key point in the GARCH approach. This financial application can be extend-ed to analyze other specific shares or stock indexes in any stock market in the world. There-fore, it is necessary to understand the operating procedures of their pricing for risk manage-ment, profitability strategies, cost minimization and, in addition, to construct the optimal port-folio depending on investor’s preferences.
An Application of Tobit Regression on Socio Economic Indicators in Gujaratijtsrd
The use of factual estimation frameworks to consider human behavior in a social environment is known as social insights. In this study researcher examined. Socio Economics indicators like Education, Health and Employment in Gujarat he also used Tobit Regression as a statistical tool. It will be found that the most of the Sub Indicators are positively impact on Tobit Regression model. Dr. Mahesh Vaghela "An Application of Tobit Regression on Socio Economic Indicators in Gujarat" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46309.pdf Paper URL : https://www.ijtsrd.com/mathemetics/statistics/46309/an-application-of-tobit-regression-on-socio-economic-indicators-in-gujarat/dr-mahesh-vaghela
The purpose of the paper is to assess the validity of two interpretations which have been used in the description of the relationship between employment growth and economic activity in Finland during the 1990s. According to the New Era view the Finnish economy has moved into a new era which, as a result of a faster-than-before rate of labour productivity growth, is characterized by "jobless growth". According to the Cyclical Rebound view no change in the rate of trend productivity growth has taken place. The productivity-led growth, which after the very deep depression characterized the recovery of the economy, only reflected a normal cyclical rebound. The main result of my investigation is as follows. Neither the New Era view nor the Cyclical Rebound view provides a telling interpretation about the developments of productivity and the relationship between output and employment growth in the 1990s. Characterizing the years of the recovery as reflecting a New Era which is associated with an increase in the rate of longrun productivity growth is misleading, because that kind of change has not taken place. On the other hand, the movements of productivity are hard to reconcile with the Cyclical Rebound view because the years from 1992 to1994, especially, were exceptional. During the period movements in productivity were not consistent with a pro-cyclical pattern, and, what is important, the productivity trend shifted upwards. However, the shift was not associated with an acceleration in the rate of trend productivity growth. The upward shift was caused by a sequence of positive technology shocks, which were identified by using a structural VAR model. The identifying restriction was rationalized by utilizing a new Keynesian dynamic general equilibrium model. The positive technology shocks which dominated the developments of aggregate productivity during the period from 1992 to 1994 mainly reflect micro-structural changes like business restructuring and labour reallocation in manufacturing
Efeitos de crescimento das reformas estruturais na Europa do Sul - 511: O cas...Cláudio Carneiro
Este trabalho desenvolve um modelo de crescimento semi-endógena para analisar os efeitos intertemporais das reformas estruturais nos países do sul da Europa (Itália, Espanha, Portugal e Grécia). O modelo segue o paradigma variedade de produtos em um ambiente semi-endógena, e inclui uma desagregação do trabalho em grupos diferentes de habilidade. Nós usamos um conjunto abrangente de indicadores estruturais, a fim de calibrar o modelo de relações macroeconômicas importantes e os níveis de produtividade e do emprego. Nossos resultados mostram que as reformas estruturais produzir ganhos econômicos significativos a médio e longo prazo. Os resultados apontam para a importância das reformas dos mercados de produtos e de reformas educacionais e fiscais do mercado de trabalho como as áreas mais promissoras de intervenções de política estrutural. Este documento também defende a colocar mais ênfase na política de educação que é fundamental na melhoria da força de trabalho, especialmente naqueles países onde a percentagem de trabalho pouco qualificado está entre as mais altas na área do euro.
The paper proposes two econometric models of inflation for Azerbaijan: one based on monthly data and eclectic, another based on quarterly data and takes into account disequilibrium at the money market. Inflation regression based on monthly data showed that consumer prices dynamics is explained by money growth (the more money, the higher the inflation), exchange rate behaviour (appreciation drives disinflation), commodities price dynamics (“imported” inflation) and administrative changes in regulated prices. For the quarterly model, nominal money demand equation (with inflation, real non-oil GDP and nominal interest rate on foreign currency deposits as predictors) and money supply equation were estimated, and error-correction mechanism from money demand equation was included into inflation equation. It is shown that disequilibrium at the money market (supply higher than demand) drives inflation together with money supply growth and nominal exchange rate depreciation and administrative changes in prices. No cost-push variables appeared to be significant in this equation specification. Both models give similar inflation projections, but sudden changes in money demand (2012) lead to significant differences between the projections. It is shown that money is the most important inflation determinant that explains up to 97.8% of CPI growth between 2012 and 2015, and that in order to keep inflation under control the Central Bank of Azerbaijan should link money supply to real non-oil GDP growth.
Authored by: Alexander Chubrik, Przemyslaw Wozniak, Gulnar Hajiyeva
Published in 2012
Shift share analysis is a traditional tool; through a descriptive analysis of the productive structure, it allows the comparison of regional differences within a country, region or state (SIMÕES, 2004).Shift-share analysis is one way to account for the competitiveness of a region's industries and to analyze the local economic base. This analysis is primarily used to decompose employment changes within an economy over a specific period of time into mutually exclusive factors. Like other analytical economic tools, the shift-share technique is only a descriptive tool that should be used in combination with other analysis to provide a summary of a region's key employment potential industries.
Location Quantities and Shift Share Analysis ProjectJacqueline Tkac
This report provides a brief review of the specific metric of employment sector analysis called Location Quotients (LQ) and Shift-Share. The analysis area focused on in this report is Richmond, Virginia, and the comparison area is the United States.
The nature of co-movement between total output and employment during the 1990s indicates that the relationship between employment growth and economic activity has been peculiar in Finland. This has been reflected, for example, in the developments of aggregate labour productivity. In particular, the years from 1992 to 1994 were exceptional. During that period productivity growth was very rapid, and, what is important, the trend of aggregate labour productivity shifted upwards. By only analysing the relationship between total output and employment it is impossible to say what happened during the period between 1992 and 1994. In this paper the relationship is analysed by utilizing industry-level data. The analysis shows that the rapid growth in aggregate productivity and the upward shift in the productivity trend mainly reflect similar developments in manufacturing, particularly in the metal industry. Even though the investigation is based on the use of industry-level data, it is still aggregative, which makes the interpretation of the results less clearcut. The existing studies which are based on the use of micro-level (e.g. plant-level) data support the interpretation which emphasizes the role of business restructuring and labour reallocation within manufacturing as the causes of rapid productivity growth and the upward shift in the trend productivity. The analysis is based on the estimation of simple structural VAR models.
Shift Share Analysis Based on Main Activity Sector of Selected Districts of B...Shahadat Hossain Shakil
Shift share analysis is an effective regional planning tool to explore the regional competitiveness and industrial composition. In this study the regional competitiveness among the selected districts of Bangladesh in terms of regional employment figure in the main activity sectors has been tried to develop. The comparative scenario among the several districts has been figured out and the regional influencing factors behind that have been analyzed.
The paper is concerned with the relationship between labour share and unemployment in the major OECD countries. Special emphasis is put on examining whether the relationship has altered in a manner which can be interpreted as an indication of the weakened bargaining power of labour. The
econometric analysis is based on the use of the theoretical framework which employs the notion of the wage curve as a central analytical tool. The investigation utilises cross-country panel data for twenty OECD countries over the period from 1972 to 2008 and statistical methods suitable for the
examination of non-stationary panels. According to the results, the decline in the labour share, which is apparent in most major OECD countries, is highly likely due, at least partly, to the weakened bargaining power of labour. With a given level of unemployment, the labour share is nowadays lower than before.
Shift share analysis is a traditional tool; through a descriptive analysis of the productive structure, it allows the comparison of regional differences within a country, region or state (SIMÕES, 2004).Shift-share analysis is one way to account for the competitiveness of a region's industries and to analyze the local economic base. This analysis is primarily used to decompose employment changes within an economy over a specific period of time into mutually exclusive factors. Like other analytical economic tools, the shift-share technique is only a descriptive tool that should be used in combination with other analysis to provide a summary of a region's key employment potential industries.
This study addresses the connection between reorganization and unemployment in the labour market. Reorganization of regional labour markets measured by simultaneous gross migration flows lowers the unemployment rate, based on evidence from a panel of Finnish regions. However, reorganization is shown to be unrelated to long-term unemployment.
The Causal Analysis of the Relationship between Inflation and Output Gap in T...inventionjournals
The purpose of the paper is to study dynamic relationships between the inflation and output gap by using Granger causality, Impulse response and variance decompositions analysis within VECM framework for the quarterly data over the first period of 2003 and second period of 2016. The results of the study indicate that the output gap Granger cause the inflation in Turkey both in short-and long-runs. Also, sign of the causality is negative and same causal relationships between two variables hold beyond the sample period. The results should be taken as an evidence of the conclusion that the output gap has important implications for the CBRT's monetary policy.
Forecasting Economic Activity using Asset PricesPanos Kouvelis
This dissertation evaluates how well the asset prices and, in particular the term spread, the short rate and the real stock returns, forecast the GDP growth and the Industrial Production. The study is applied with data of seven countries (Canada, France, Germany, Italy, Japan, United Kingdom and United States) and it covers a period of time between 1966 until now. The research finds that the asset prices have forecasting power for one quarter/month but they lose their power when the forecasting horizon increases. Moreover, the paper evaluates that the real stock return is the best predictor of the GDP growth and that the short rate has more predictive content than the term spread.
Keywords: Term spread, short rate, stock returns, output growth, forecasting horizon, out-of-sample statistics
The paper proposes two econometric models of inflation for Azerbaijan: one based on monthly data and eclectic, another based on quarterly data and takes into account disequilibrium at the money market. Inflation regression based on monthly data showed that consumer prices dynamics is explained by money growth (the more money, the higher the inflation), exchange rate behaviour (appreciation drives disinflation), commodities price dynamics (“imported” inflation) and administrative changes in regulated prices. For the quarterly model, nominal money demand equation (with inflation, real non-oil GDP and nominal interest rate on foreign currency deposits as predictors) and money supply equation were estimated, and error-correction mechanism from money demand equation was included into inflation equation. It is shown that disequilibrium at the money market (supply higher than demand) drives inflation together with money supply growth and nominal exchange rate depreciation and administrative changes in prices. No cost-push variables appeared to be significant in this equation specification. Both models give similar inflation projections, but sudden changes in money demand (2012) lead to significant differences between the projections. It is shown that money is the most important inflation determinant that explains up to 97.8% of CPI growth between 2012 and 2015, and that in order to keep inflation under control the Central Bank of Azerbaijan should link money supply to real non-oil GDP growth.
Authored by: Alexander Chubrik, Przemyslaw Wozniak, Gulnar Hajiyeva
Published in 2012
Shift share analysis is a traditional tool; through a descriptive analysis of the productive structure, it allows the comparison of regional differences within a country, region or state (SIMÕES, 2004).Shift-share analysis is one way to account for the competitiveness of a region's industries and to analyze the local economic base. This analysis is primarily used to decompose employment changes within an economy over a specific period of time into mutually exclusive factors. Like other analytical economic tools, the shift-share technique is only a descriptive tool that should be used in combination with other analysis to provide a summary of a region's key employment potential industries.
Location Quantities and Shift Share Analysis ProjectJacqueline Tkac
This report provides a brief review of the specific metric of employment sector analysis called Location Quotients (LQ) and Shift-Share. The analysis area focused on in this report is Richmond, Virginia, and the comparison area is the United States.
The nature of co-movement between total output and employment during the 1990s indicates that the relationship between employment growth and economic activity has been peculiar in Finland. This has been reflected, for example, in the developments of aggregate labour productivity. In particular, the years from 1992 to 1994 were exceptional. During that period productivity growth was very rapid, and, what is important, the trend of aggregate labour productivity shifted upwards. By only analysing the relationship between total output and employment it is impossible to say what happened during the period between 1992 and 1994. In this paper the relationship is analysed by utilizing industry-level data. The analysis shows that the rapid growth in aggregate productivity and the upward shift in the productivity trend mainly reflect similar developments in manufacturing, particularly in the metal industry. Even though the investigation is based on the use of industry-level data, it is still aggregative, which makes the interpretation of the results less clearcut. The existing studies which are based on the use of micro-level (e.g. plant-level) data support the interpretation which emphasizes the role of business restructuring and labour reallocation within manufacturing as the causes of rapid productivity growth and the upward shift in the trend productivity. The analysis is based on the estimation of simple structural VAR models.
Shift Share Analysis Based on Main Activity Sector of Selected Districts of B...Shahadat Hossain Shakil
Shift share analysis is an effective regional planning tool to explore the regional competitiveness and industrial composition. In this study the regional competitiveness among the selected districts of Bangladesh in terms of regional employment figure in the main activity sectors has been tried to develop. The comparative scenario among the several districts has been figured out and the regional influencing factors behind that have been analyzed.
The paper is concerned with the relationship between labour share and unemployment in the major OECD countries. Special emphasis is put on examining whether the relationship has altered in a manner which can be interpreted as an indication of the weakened bargaining power of labour. The
econometric analysis is based on the use of the theoretical framework which employs the notion of the wage curve as a central analytical tool. The investigation utilises cross-country panel data for twenty OECD countries over the period from 1972 to 2008 and statistical methods suitable for the
examination of non-stationary panels. According to the results, the decline in the labour share, which is apparent in most major OECD countries, is highly likely due, at least partly, to the weakened bargaining power of labour. With a given level of unemployment, the labour share is nowadays lower than before.
Shift share analysis is a traditional tool; through a descriptive analysis of the productive structure, it allows the comparison of regional differences within a country, region or state (SIMÕES, 2004).Shift-share analysis is one way to account for the competitiveness of a region's industries and to analyze the local economic base. This analysis is primarily used to decompose employment changes within an economy over a specific period of time into mutually exclusive factors. Like other analytical economic tools, the shift-share technique is only a descriptive tool that should be used in combination with other analysis to provide a summary of a region's key employment potential industries.
This study addresses the connection between reorganization and unemployment in the labour market. Reorganization of regional labour markets measured by simultaneous gross migration flows lowers the unemployment rate, based on evidence from a panel of Finnish regions. However, reorganization is shown to be unrelated to long-term unemployment.
The Causal Analysis of the Relationship between Inflation and Output Gap in T...inventionjournals
The purpose of the paper is to study dynamic relationships between the inflation and output gap by using Granger causality, Impulse response and variance decompositions analysis within VECM framework for the quarterly data over the first period of 2003 and second period of 2016. The results of the study indicate that the output gap Granger cause the inflation in Turkey both in short-and long-runs. Also, sign of the causality is negative and same causal relationships between two variables hold beyond the sample period. The results should be taken as an evidence of the conclusion that the output gap has important implications for the CBRT's monetary policy.
Forecasting Economic Activity using Asset PricesPanos Kouvelis
This dissertation evaluates how well the asset prices and, in particular the term spread, the short rate and the real stock returns, forecast the GDP growth and the Industrial Production. The study is applied with data of seven countries (Canada, France, Germany, Italy, Japan, United Kingdom and United States) and it covers a period of time between 1966 until now. The research finds that the asset prices have forecasting power for one quarter/month but they lose their power when the forecasting horizon increases. Moreover, the paper evaluates that the real stock return is the best predictor of the GDP growth and that the short rate has more predictive content than the term spread.
Keywords: Term spread, short rate, stock returns, output growth, forecasting horizon, out-of-sample statistics
This paper investigates the link between forecast disparity and macroeconomic instability that results from the data revision of GDP and inflation in Japan. The recent Japanese case, which reflects the unconventional monetary policy conducted since 2013, is also examined. The empirical results show that such disparities do not cause economic instability; however, they have have done so after the unconventional and drastic monetary policy was conducted. On the other hand, exchange rates impacted economic stability for the total period. For the first part of the period under study (from 2000 to 2012), currency appreciation caused instability; however, for the more recent period, depreciation has caused such instability. Recently, macroeconomic instability has been linked with exchange rate movements.
Developing economies are different than developed economies in many aspects, i.e., in terms of institutional framework and political situation etc. Thus, the monetary policy needed in developing countries is also different than developed countries. The goal of this study is to investigate exchange rate channel of monetary transmission mechanism in a developing country’s setup. The variables included in our analysis are interest rate, exchange rate, exports, consumer price index and gross domestic product. Johansen cointegration technique is applied to analyze the long run relationship among variables while multivariate VECM granger causality test is used to explore the direction of causality among the set of our variables. We use annual data ranging from 1980 to 2015 while taking account of the limitations of time series data. Our findings suggest that output has a negative long run relationship with exchange rate and interest rate, positive relationship with exports and no statistically significant relationship with inflation. Interest rate granger causes all four of our variables thus showing the power of this policy tool. Exchange rate causes exports, consumer price index and output which means exchange rate is the second most powerful variable in our analysis. Output is granger caused by interest rate, exports and exchange rate which confirms the sensitivity of output to these variables. Consumer price index is granger caused by all four of our variables and came out to be the most sensitive variable in our analysis.
This study aims to analyze the level of efficiency of local government health sector sending during
the Covid-19 pandemic and productivity in the health sector. The measurement of efficiency and productivity
values was obtained using the Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI)
analysis methods. The assumptions used are Variable return to scale (VRTS) and output oriented. The results of
the study indicate
This paper develops a forward-looking indicator for macroeconomic uncertainty that employers are confronted with when they take decisions about the size of their workforce. The model that provides the basis for this uncertainty indicator interprets hires and lay-offs of workers as an investment into projects with uncertain return. Employers decide when to undertake this investment. Uncertainty can then be derived as a function of a labour productivity threshold above which it is profitable for employers to hire workers. The measure that is first theoretically derived is then taken to the data. Economy-wide uncertainty for G7 economies and uncertainty by economic sector for the United States are calculated from data on hiring demand and unit labour costs. The resulting quarterly time series demonstrate that in most economies hiring uncertainty went up at the onset of the Great Recession and has remained at an elevated level since then. A panel VAR analysis reveals that hiring uncertainty excercises a significant, economically sizeable and persistent effect on both the output gap and unemployment.
Applying the Bootstrap Techniques in Detecting Turning Points: a Study of Con...FGV Brazil
Applying the Bootstrap Techniques in Detecting Turning Points: a Study of Consumer Sentiment Survey - 2014
The purpose of this study, FGV’s Brazilian Institute of Economics (IBRE), is to improve the ability of the Consumer Confidence Index (CCI) of detecting turning points by shorting the statistical confidence interval by applying the Bootstrap Technique to the Consumers Survey.
This study examined the influence of the characteristics of the audit committee on Palestinian firms’ value. The research explores precisely the effect on the Audit Committee characteristics’ efficiency, namely, independence, expertise, evaluating the relationship among dependent and independent variables. Secondary data collected from a list of companies were registered in the Palestine Stock Exchange from 2011 to 2018. Individual variables considered are the independence & expertise of the audit committee, whereas the ROA is employed as the dependent variable as an indicator of a firm’s value. The results showed that the Audit Committee’s independence & expertise substantially positive with ROA. The study concluded that the audit committee’s characteristics are enhancing firm performance. The implications of this study’s findings can be used by decisions and policymakers, the firm’s management, and other stockholders’ interests to create reliable ties between agents and the principals.
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict, and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates a strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends conflict management as an integral part of effective human resource management.
This paper examines the role of loan characteristics in mortgage default probability for different mortgage lenders in the UK. The accuracy of default prediction is tested with two statistical methods, a probit model and linear discriminant analysis, using a unique dataset of defaulted commercial loan portfolios provided by sixty-six financial institutions. Both models establish that the attributes of the underlying real estate asset and the lender are significant factors in determining default probability for commercial mortgages. In addition to traditional risk factors such as loan-to-value and debt servicing coverage ratio lenders and regulators should consider loan characteristics to assess more accurately probabilities of default.
This study examined the impact of financial innovation on money demand in Nigeria, using quarterly time series for the period 2009-2019. The dependent variable was money demand, represented by broad money, while the independent variable was financial innovation represented by modern payment channels such as volume of Automated Teller Machines (ATMs) transactions, volume of Point of Sales (POS) transactions, volume of Internet banking transactions, and volume of Mobile banking transactions. The study employed the ordinary least squares (OLS) regression technique as the estimation method within the cointegration, granger causality, and error correction modeling. The result obtained showed that financial innovation has mixed impact on money demand in Nigeria during the period of analysis. For instance, financial innovation has positive impact on money demand through volume of ATM transactions in the current period, two periods lagged of volume of mobile banking transactions, current period and one period lagged of volume of internet banking transactions, and current period’s volume of Point of Sales (POS) transactions in Nigeria. On the other hand, financial innovation has negative impact on money demand through one period lagged of volume of point of sales in Nigeria. On the stability of the demand for money function, the result of the stability tests based on the CUSUM test and CUSUM of squares test showed that the demand for money function was stable during the evaluation period. The study recommended that monetary policy strategy of the central bank of Nigeria (CBN) should be fine-tuned to ensure it is well suited to deal with the challenges posed by financial innovation by way of proliferation of sophisticated payment channels.
Equity financing is one of the sources of funding available to non-bank financial institutions which is quite prevalent in developed financial markets for small or start-up firms. This study empirically determined the effect of the Equity Financing Scheme on a sustainable increase in productivity of agro-allied small businesses in Nigeria. Data for this study were elicited through the use of a questionnaire structured in a five-point likert scale. The evaluation of the relationship between the dependent and independent variables was performed using the Ordinary Least Square regression technique. The study revealed that the equity financing scheme had a positive and significant effect on the sustainable productivity of agro-allied small businesses in South-South Nigeria. The study recommended that efforts should be made to educate the small business entrepreneurs on the benefits of equity financing as a viable option towards business growth and expansion and that the government through the various intervention agencies should restructure the long-term loan policies to give access to more growth-oriented agro-allied businesses, to increase their presently low capacity to procure heavy-duty technology to increase productivity and achieve food security in Nigeria. Small business owners should take advantage of the membership of cooperative societies and as well maintain good business relationships with suppliers; this will guarantee a continuous supply of needed materials and uninterrupted operations of the business.
This study seeks to evaluate the impact of public borrowing on economic growth in Nigeria using time series data from 1980 to 2018. Specifically, the study seeks to analyze the effect of domestic debt (proxy by Federal Government Bonds-FGB) and external debt (proxy by International Monetary Fund Loan-IMFL) on Nigerian’s Gross Domestic Product (GDP). To achieve this objective, secondary data was collected from the Central Bank of Nigeria Statistical bulleting and the Debt Management Office of Nigeria. A multiple regression model involving the dependent variable (GDP) and the independent variables (FGB and IMFL) was formulated and subjected to econometric analysis. These variables were adjusted with the Jarque-bera test of normality while the correlation result was used to check the possibility of multi-collinearity among the variables. The t-test was used to answer the research questions and test the formulated hypotheses at the 5percent statistical level. Results from the analysis show that a positive relationship exists between IMF Loan and Nigeria’s gross domestic product, while a negative relationship exists between FG Bonds and Nigeria’s gross domestic product, which violates the Keynesian theory of public debt. The study concludes that both domestic and external debt significantly affect economic growth in Nigeria. Therefore, it was recommended that public borrowing should be efficiently used and contracted solely for economic reasons and not for social or political reasons as this will help to avoid accumulation of debt stock over time.
Equity investment financing is an innovative way of financing the real sector which has considerable developmental potential. The study empirically determined the effect of Equity investment financing on sustainable increase in productivity among agro-allied small businesses in South-South Nigeria. The instrument of data collection is the research questions structured in a five-point likert scale. The evaluation of the relationship between the dependent and independent variables was performed using the Ordinary Least Square regression technique. The study revealed that equity investment financing has a positive and significant effect on the sustainable productivity of businesses in Nigeria. The study recommended educating small business entrepreneurs on the benefits of equity financing as a viable option towards business growth and expansion and that the government through the various intervention agencies should restructure the long-term loan policies to give access to more growth-oriented agro-allied businesses, to increase their presently low capacity to procure heavy-duty technology to increase productivity and achieve food security in Nigeria. Small business owners should take advantage of the membership of cooperative societies and as well maintain good business relationships with suppliers; this will guarantee a continuous supply of needed materials and uninterrupted operations of the business.
This paper aims to explore the relationships of the performance of producer responsibility organizations (PROs) for waste oil, waste electrical and electronic equipment (WEEE), and end-of-life vehicles (ELV). The methodology consists in estimating the cointegration equations between the variables of lubricating oil production (SIG), electric and electronic equipment (EEE), and vehicle production (VP) using dynamic ordinary least squares (DOLS). Subsequently, elasticities are got based on estimates for Spain over the period 2007-2019 using quarterly data. The main results were that SIG and EEE were cointegrated variables. The elasticity of the SIG variable up to EEE was positive at 2, 4166. Additionally, the elasticity of the SIG variable up to VP was 2, 4050. However, SIG and VP are not cointegrated variables; subsequently, it was not a stable relationship between these variables. Results suggest it was because EPR was applied in WEEE PRO join with a deposit refund system (DRS); meanwhile, EPR in ELV PRO had been applied without subsidies to purchase cars.
In the process of R&D globalization, due to market demand and preferential policies, many multinational companies choose to invest in R&D in China. With the increase of labor costs in coastal areas and the rapid economic development of the central and western regions, multinational companies have already shifted from coastal areas to central and western regions when choosing R&D regions in China, especially in Shaanxi Province. Therefore, studying the character of R&D investment and operating performance of Multinational Corporation in Shaanxi Province has important practical significance. This article uses the data of the R&D investment of multinational corporation in the joint annual inspection of Shaanxi Province in 2018 as the sample and uses EXCEL software to conduct data analysis to gain an in-depth understanding of the character of R&D and investment of multinational corporation in Shaanxi Province, business characteristics and business performance. And it is concluded that the R&D investment of multinational corporation in Shaanxi Province has a series of characteristics such as concentration of distribution, concentration of enterprise scale, and overall good performance of operating performance.
In Bangladesh, migrant worker’s remittances constitute one of the most significant sources of external finance. This paper investigates the existence of relation between remittance inflow and GDP and the causal link between them in Bangladesh by employing the Granger causality test under a VECM framework. Using time series data over a 38 year period, we found that growth in remittances does lead to economic growth in Bangladesh. In addition to the relationship, this paper also points out some issues that are working as impediments in getting remittance and give some recommendations to overcome those impediments.
In the context of the 4.0 revolution, technology applications, especially cloud computing will have strong impacts on all areas, including accounting systems of enterprises. Cloud computing contributes to helping the enterprise accounting apparatus become compact, help automate the input process, improve the accuracy of the input data. Besides, the issur of accounting, reporting, risk control and information security also became better, contributing to improving the effectiveness of accounting. However, besides the positive impacts, businesses also face many difficulties in deploying and applying cloud computing. However, this application requirement will become an inevitable trend contributing to improving the operational efficiency of enterprises. To promote this process requires from the State as well as businesses themselves must have awareness and appropriate decisions. Breakthroughs in information technology have dramatically changed the accounting industry and the creation of financial statements. The Internet and the technologies that use the power of the Internet are playing an important role in the management and accounting activities of businesses - who always tend to be ready to receive and use public innovations technology in collecting, storing, processing and reporting information.
In recent years, Vietnam has joined international intergration by strong export agreements of bilateral and multilateral; Vietnam’s merchandise export in 1995 was only US $5.4 billion, in 2018 Vietnam’s merchandise export increased by 45 times compared to 1995 with US $244 billion. Vietnam’s imports increased by 29 times in 2018 compared to 1995. This study is an attempt to test a method of estimating the influence of exports on several Supply-sidefactors such as production value, value added and imports through the expansion of the standard system W. Leontief I.O and Miyazawa-style economic-demographic relations. This study also tries to make an experiment in the “Leontief Paradox”.The result is that Vietnam’s export value spread to production and imports but spread low to added value, especially in the processing industry group’s fabrication. The study is based on the non-competitive I.O table in 2012 and 2018 with 16 sectors.
The profitability of commercial banks is influenced by a number of internal and external factors. This paper attempts to identify the internal factors which significantly influence the profitability of commercial banks in Bangladesh. In this study, profitability is measured by ROA and ROE which may be significantly influenced by the internal factors such as IRS, NIM, CAR, CR, DG, LD, CTI and SIZE of the bank. Data are collected from published annual reports during 2014--2018 of 23 commercial banks. Using simple regression model, it is found that CR has significant effect on the profitability and CAR has significant influence on ROA only. In addition to this, DG has significant effects on PCBs’ profitability (ROE only) where as IRS and CTI have significant influence on profitability (ROA only) of ICBs. Further, none of these variables have significant effects on the profitability of SCBs but CAR and CR are correlated with profitability (ROA only) and the causes may be the nature of services provided by SCBs to its clients. The internal policy makers should manage the influential internal factors of the banks in order to increase their profitability so that they can meet stakeholders’ expectations.
Using a series of econometric techniques, the study analysed interaction between monetary policy and private sector credit in Ghana. This study made use of monthly dataset spanning January 1999 to December 2019 of credit to the private sector (PSC) and broad money supply (M2). The results reveal that there exists cointegration, a long run stationary relation between monetary policy and private sector credit. This implies, increases in credit should prompt long-term increases in monetary policy. It is not surprising that growth in the private sector might have a stronger effect on monetary policy. The Error Correction Test is statistically significant and that all the variables demonstrate similar adjustment speeds. This implies that in the short run, both money supply and credit are somewhat equally responsive to their last period’s equilibrium error. There is unidirectional causation from private sector credit to monetary policy. It can be said that, there is an interaction between money supply and private sector credit. Thus, credit to private sector holds great potential in promoting economic growth. It can be recommended to the government to increase the credit flow to the private sector because of its strategic importance in creating and generating growth of the economy.
This paper investigates if forecasting models based on Machine Learning (ML) Algorithms are capable to predict intraday prices in the small, frontier stock market of Romania. The results show that this is indeed the case. Moreover, the prediction accuracy of the various models improves as the forecasting horizon increases. Overall, ML forecasting models are superior to the passive buy and hold strategy, as well as to a naïve strategy that always predicts the last known price action will continue. However, we also show that this superior predictive ability cannot be converted into “abnormal”, economically significant profits after considering transaction costs. This implies that intraday stock prices incorporate information within the accepted bounds of weak-form market efficiency, and cannot be “timed” even by sophisticated investors equipped with state of the art ML prediction models.
Applying the Arrow-Debreu-Mundell-Fleming model as an economic standard model, with combining axiological framework and epistemological model, it is proposed to analyze economic policies with using a synthetic model, where interest, exchange and tax rates are integrated together. Except normal monetary and fiscal policies mainly via interest and tax rates, there are feasible ways to utilize modified strategies via exchange and tax rates. When ones need to simulate national local market, ones can raise the exchange rate. Otherwise, when ones need to promote international global trade, ones may lower the exchange rate. It is found that tax reduction is good policy when tax rate is higher than normal and that tax increase is good social policy when tax rate is lower than normal, during economic depression. Also it is revealed that tax reduction is good social policy when tax rate is lower than normal, and that tax increase is good policy when tax rate is higher than normal, during economic overheat. While economic system seeks efficiency and social system pursues equality, common interest modifications with elastic exchange and tax rates could be applied for balancing efficiency and equality.
In recent times, agricultural sector has returned to the forefront of development issues in Nigeria given its contribution to employment creation, sustainable food supply and provision of raw materials to other sectors of the economy. In lieu of that, this study examines the impact of agriculture on the economic growth in Nigeria using annual time series data covering the sample period of 1981 to 2018. To analyse the data collected, Autoregression Distributed Lag (ARDL) model through the bounds testing framework is employed to measure the presence of cointegrating relations between real GDP, agricultural productivity, labour force, and agricultural export. Results show the presence of both short-run and long-run relationship among the variables, and that agriculture has a positive and significant impact on economic growth in Nigeria. These findings inform the Nigerian government on the need to expedite labour force (human capital) and agricultural export (non-oil) development with the view to achieving sustainable growth and development. In addition, developing skills and competencies of labour force through capacity building in the agricultural sector will encourage research and development thereby increase the export size, hence essential for long-term growth.
The article illustrates the results of the economic development of the first fifteen years of the XXI century under the conditions of unprecedented economic freedom, globalization and the appearance of new informational sectors up to and including the first attempts at revising liberalism. The analysis of statistical data demonstrates an obvious increase in the percentage of well-off people in many countries as well as the increased economic capabilities of small, medium and large businesses, whose assets are distributed among an ever-increasing number of owners. This provides the impetus to review our collective approach to liberalization and globalization, as well as to view its unexpected strong sides that make human progress possible.
This paper investigates the relationship between working capital management and financial performance of Pharmaceuticals and Textile firms listed at the Dhaka Securities Exchange in Bangladesh. The data analysis was carried on ten Pharmaceuticals and Textile firms for a period of 2013 to 2017. Secondary Data was analyzed by applying Descriptive Statistics, Regression and Correlation analysis to findthe relationship of current ratio, inventory conversion period and average payment period with Return on Asset. The findings indicate that the Pharmaceuticals and Textile firms’ performance is influenced by the variables relating to working capital. There is a positive relationship between profitability and current ratioand Inventory Turnover period shows a negative relationship with profitability but Average payment period shows insignificant impact on profitability. The study concludes that there exists a relationship between working capital managementand financial performance of Pharmaceuticals and Textile firms in Bangladesh. The study recommends that for the Pharmaceuticals and Textile firms to remain profitable, they should employ working capital management practice that will help in making decisions about investment mix and policy, matching investment to objective, asset allocation for institution and balancing risk against profitability.
Organizational behaviour involves the design of work as well as the psychological, emotional and interpersonal behavioural dynamics that influence organizational performance. Management as a discipline concerned with the study of overseeing activities and supervising people to perform specific tasks is crucial in organizational behaviour and corporate effectiveness. Management emphasizes the design, implementation and arrangement of various administrative and organizational systems for corporate effectiveness. While the individuals, and groups bring their skills, knowledge, values, motives, and attitudes into the organization, and thereby influencing it, the organization, on the other hand, modifies or restructures the individuals and groups through its structure, culture, policies, politics, power, and procedures, and the roles expected to be played by the people in the organization. This study conducted through the exploratory research design involved 125 participants, and result showed strong positive relationship between the variables of interest. The study was never exhaustive due to limitations in terms of time and current relevant literature, therefore, further study could examine the relationship between personality characteristics and performance in the public sector, where productivity is not outstanding, when compared with the private sector. Based on the result of this investigation it was recommended that organizations should provide emotional intelligence programmes for their membership as an important pattern of increasing co-operative behaviours and corporate effectiveness.
More from International Journal of Economics and Financial Research (20)
What price will pi network be listed on exchangesDOT TECH
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The European Unemployment Puzzle: implications from population agingGRAPE
We study the link between the evolving age structure of the working population and unemployment. We build a large new Keynesian OLG model with a realistic age structure, labor market frictions, sticky prices, and aggregate shocks. Once calibrated to the European economy, we quantify the extent to which demographic changes over the last three decades have contributed to the decline of the unemployment rate. Our findings yield important implications for the future evolution of unemployment given the anticipated further aging of the working population in Europe. We also quantify the implications for optimal monetary policy: lowering inflation volatility becomes less costly in terms of GDP and unemployment volatility, which hints that optimal monetary policy may be more hawkish in an aging society. Finally, our results also propose a partial reversal of the European-US unemployment puzzle due to the fact that the share of young workers is expected to remain robust in the US.
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USDA Loans in California: A Comprehensive Overview.pptxmarketing367770
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Measures of the Output Gap in Turkey: An Empirical Assessment of Selected Methods
1. International Journal of Economics and Financial Research
ISSN(e): 2411-9407, ISSN(p): 2413-8533
Vol. 5, Issue. 11, pp: 238-251, 2019
URL: https://arpgweb.com/journal/journal/5
DOI: https://doi.org/10.32861/ijefr.511.238.251
Academic Research Publishing
Group
*Corresponding Author
238
Original Research Open Access
Measures of the Output Gap in Turkey: An Empirical Assessment of Selected
Methods
İlyas Şıklar*
Anadolu University, Department of Economics, Turkey
Suzan Şahin
Anadolu University, Department of Economics, Turkey
Abstract
The output gap indicating the difference between the actual and potential levels of output is a critical factor for
estimating the inflationary pressures in an economy. If the main target of a central bank is ensuring and maintaining
the price stability, estimating the output gap with a minimum error is crucial for the efficiency of the monetary
policy. In this study, we estimated the output gap in Turkey for the 2002-2014 period by using four different
methods. Two of these estimation methods are purely statistical (Linear Trend and Hodrick-Presscot (HP) Filtering)
while the others are integrated with the relations suggested by the economic theory (multivariate structural model
and structural autoregressive (SVAR) model). By using empirical decision criteria common in the literature, we
conclude that SVAR model produces the most reliable output gap estimates to explain inflationary pressures in
Turkey. However, we also found that the Hodrick-Presscot filtering method is the second best methodology in the
output gap estimation process.
Keywords: Output gap; Potential GDP; HP filter; SVAR model; Linear trend model; Multivariate structural model.
CC BY: Creative Commons Attribution License 4.0
1. Introduction
The output gap is defined as the difference between the actual output and the potential output. The calculation of
the output gap with a minimum error is important for the effectiveness of the monetary policy. If the real output is
higher than the potential, that is, if the output gap is positive, the resulting demand pressure may be at a level that
would lead to inflation. Therefore, the output gap is considered as a sign of demand-side inflationary pressure by the
policy makers.
‘Potential output’ is a concept which refers to a long-term process, while ‘output gap’ refers to a short or
medium-term process. Demand-side shocks affect output gap by effecting real output, while supply-side shocks
affect potential output. While the Gross Domestic Product (GDP), a component of the output gap, refers to realized
output, exact calculation of the other component (i.e. the potential output) is difficult both in theory and in practice.
Therefore, various methods have been developed to estimate the potential output. These methods have a wide
spectrum ranging from univariate statistical filters to multivariate structural models. Because of their nature,
statistical models can be established by using less information compared to theory-based structural models. Their
prominent characteristic therefore is the ease of application. Despite statistical models are highly preferred, the
choice of the correction parameters or initial values used in the univariate statistical filter methods result in different
estimation outcomes. Another criticism claims that these methods are not based on economic theory. Structural
models are both multivariate and stemmed from the economic theory. Furthermore, they contain additional
information on growth dynamics. For these reasons, they are advocated as alternative calculation methods against
the criticisms on statistical models. This is why structural models in the literature are more common. Structural
models give an opportunity to obtain output gap estimates which are suitable for stationary inflation assumptions.
The main aim of the present study is to determine the estimation method that best predicts the effect of output
gap on inflation. In this context, four of the most commonly used methods are chosen; two statistical methods and
two structural methods. The estimates are made by using the quarterly data obtained for Turkey in 2002-2014 period.
The statistical methods are Hodrick Prescott (HP) Filter model and Linear Trend model, and the structural ones are
Multivariate Structural Model and Structural Autoregressive (SVAR) model. In our study, output is represented by
the GDP in constant prices. In this context, quarterly frequency series including 2002-2014 years are used. The base
year for this time-series is 2007, as determined by the Turkish Statistical Institute. Although the new GDP data for
the quarterly frequency of our country are accessible since 1998, the reason why we focus on the period between
2002-2014 in this study is to exclude the financial crisis experienced in 2001 from the review period. Thus, the
possibility a structural break due to the crisis is excluded from the model. A data used in the study have been
collected from the Electronic Data Delivery System of the Central Bank of the Republic of Turkey (CBRT).
The rest of the paper is organized as follow: Part 2 shortly reviews the definition and importance of potential
output and output gap while Part 3 essentially deals with output gap modeling and estimation methodologies. Part 4
empirically evaluates the forecasting performance of the output gap estimates and finally Part 5 concludes the study.
2. International Journal of Economics and Financial Research
239
2. The Definition and Importance of Potential Output and Output Gap
The term potential output was coined by M. Arthur Okun in 1962, and defined as the level of output under full
employment conditions. According to another definition by Okun, potential output shows the level of the use of
production factors which does not cause inflationary pressures. In its recent use, potential output refers to the level of
sustainable real GDP. Potential output is also a key concept for economic policy since it is used to measure the
standards of sustainable living (Horn et al., 2007).
Various methods can be used to calculate the potential output. Although the literature continues to change and
develop on this topic, the following methods are worth mentioning (the first four of them are predominantly
statistical, and the others are predominantly economic measures): Deterministic Trend, Univariate Filtering,
Unobserved Components, Multiple Filtering, Structural VAR (Blanchard-Quah), Production Function, and
Macroeconomic Models (Yavan and ve Türker Kaya, 2007).
The output gap is a critical concept used in inflation targeting monetary policy. The inflation targeting is based
on the assumption that prices and wages are sticky in short-term. When determining inflation target, central banks
use short-term interest rate as monetary policy instrument. This interest rate influences the national output gap and
the change in output gap also affects the inflation rate (Çiçek, 2009).
3. The Output Gap Modeling and Estimation for Turkey
As mentioned earlier, output gap is defined as the difference between actual output and the potential output.
Here, gap is the output gap; y is the actual output and yT
is the potential output. In such formulation, a positive
value for the gap indicates excessive demand, and a negative value indicates overcapacity. The presence of the
output gap indicates that there is a temporary deviation from the potential output level. The level of potential output
should be estimated since it is an unobservable economic variable. Because there are various definitions and
methods on this issue, various results can be obtained.
According to Brouwer and Ericsson (1995), Debelle and Vickery (1997), output gap is an economic variable
that contains highly valuable information on fluctuations in prices and wages. However, from an economic policy
perspective, the trend or potential component of the mentioned variables must be defined in terms of a constant
inflation rate. This cannot be done with the estimation of the output gap with pure statistical techniques, but it can be
applied in multivariate models and structural models. In other words, structural models make it possible to obtain
output forecasts consistent with constant inflation assumptions.
Figure 1 below shows the course followed by the logarithmic level of real GDP during the review period, while
Figure 2 indicates the quarterly and annual (four-quarter) economic growth rates in the same period.
Figure-1. Real GDP (logarithmic level)
Figure-2. Quarterly and Annual Economic Growth Rates
Note: QGLAY represents quarterly and the AGLAY represents annual growth rate
18.0
18.1
18.2
18.3
18.4
18.5
18.6
18.7
02 03 04 05 06 07 08 09 10 11 12 13 14
SEKIL_1
-.100
-.075
-.050
-.025
.000
.025
.050
.075
.100
02 03 04 05 06 07 08 09 10 11 12 13 14
QGLAY AGLAY
3. International Journal of Economics and Financial Research
240
According to the graphs, the economy experienced a serious recession in 2008-2010 due to the global crisis in
2008. On the other hand, the existence of a contraction period observed in 2012 can also be observed. The
contraction in 2008, however, was short-term, and the contraction in 2012 appears to be longer. Our general
expectation is that these two sub-periods resulted in negative output gaps with similar characteristics during the
entire review period. In the following section, we will look through the methods that we use to estimate output gap
and the results they generate.
3.1. Estimation of Output Gap by Statistical Techniques
There are a number of methods that can be used when the output gap is estimated by univariate statistical time
series techniques. These methods range from simple to complex in various scales, from the simple trend method to
ARIMA (Box-Jenkins) model, from the simple Hodrick-Prescott filtering to multi-variate Kalman filtering. Since
our goal is not to compare the performance of univariate time series techniques with structural (multivariate) models,
we prefer the most common of the above-mentioned techniques to compare the output gap bias estimates of
structural models. Hence, the output gap will be estimated by using the linear time trend and Hodrick-Prescott (HP)
filtering methods.
3.1.1. Linear Time Trend Model
The simplest way to estimate the output gap is to estimate potential output level with linear trend. Estimated
trend equation with the use of the logarithmic real GDP series for the quarterly data is as follow:
yt = 17,93 + 0,01(trend)
(0,02) (0,001)
Adj R2
= 0,94
Here, the values in parentheses express the standard error values and Adj R2
is the determination coefficient
adjusted for degrees of freedom. According the equation, the trend growth rate of output is estimated to be around
4.5 percent annually in the 13-year-period. It is possible to see the potential and actual output values obtained as a
result of this trend equation in Figure 3 while the output gap values obtained from the relation expressed by this
equation are given in Figure 4.
Figure-3. Actual and Linear Trend Potential Output Values
Note: LAY refers to real GDP and the LAYF refers to potential GDP
Figure-4. Output Gap Estimation with Linear Trend Method
18.0
18.1
18.2
18.3
18.4
18.5
18.6
18.7
02 03 04 05 06 07 08 09 10 11 12 13 14
LAY LAYF
-.08
-.06
-.04
-.02
.00
.02
.04
.06
.08
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This model can be criticized because of the fact that it is based on the estimation period of the forecasting
dimension. For example, the trend equations obtained when we move the initial period of the model from the first
quarter of 2002 to the fourth quarter of 2005 by moving one quarter onwards at a time, and the estimated output gap
values for the last quarter of 2014 can be seen in Figure 5.
Figure-5. The Output Gap Estimations by Different Initial Terms
As can be seen in the Figure 5, when the whole period is taken into account, the potential output is larger than
the actual output in the last quarter of 2014, relative to the respective trend values and there is negative output gap.
However, when we take the next quarter as the initial period, the deficit is almost zero, and even actual output is
greater than potential output. This situation continues to increase until the beginning of the third quarter of 2003 and
the estimated output gap values for the last quarter of 2014 turn out to be positive. However, when the beginning of
the forecast period is shifted further, the situation is reversed. Moreover, the model is causing us to estimate again
negative output gap values for the last quarter of 2014. So, in the prediction of the linear trend models, the choice of
the starting and ending points of the forecast period is critical. This is important because we point out that starting
the forecast period in 2002 is a good choice. On the other hand, the assumption that the potential output grows at a
constant rate is not a presumptive assumption. It is more realistic to accept that in a developing economy like Turkey
factors that affect potential output may change over time in a country where significant structural reforms are at
stake. This fact is evident in the characteristics of the predicted output gap time series. If the output is determined by
a deterministic trend, residual terms obtained as a result of eliminating this trend from the time series are expected to
have stationary time series characteristics. However, if the output-related time series is an integrated series at the
first order, in other words, if it follows a stochastic trend, then the residual series obtained by the elimination of
linear trend off will be a series of non-stationary time periods. In this case, the assumption that the output gap is a
mean reverting variable is violated. The literature on whether output follows a deterministic trend or not, on whether
it involves structural breaks or not, and on whether it has a stochastic trend or not, is so wide that it prevents a
definitive judgment (Diebold and Senhadji, 1996).
Table 1 below shows some statistics on real GDP in Turkey between 2002 and 2014, the review period.
According to the first two rows of the table, output follows a stochastic trend. However, the level of probability that
affects this decision is very low. In the third and fourth lines of the table, it can be seen that the output gap time
series estimated through linear trends are not stable at both the level and the first difference. These results are similar
to those obtained by Hodrick and Prescott (1997), and emphasize the need to apply various purification techniques.
Table-1. Unit Root Test Results for Output and Output Gap
Variable Constant t-ratio Trend t-ratio Lag t-ratio
(ADF)
Marginal
Significance Level
y 1,426 2,058 0,001 1,865 -0,079 -2,047 0,562
Δy 0,008 3,440 -0,000 -1,200 -0,500 -3,780 0,026
gap 0,007 2,057 -0,000 -2,004 -0,105 -2,483 0,335
Δgap 0,003 0,919 -0,000 -0,979 -0,441 -2,574 0,293
3.1.2. Hodrick – Prescott (HP) Filter
This purification technique, proposed by Hodrick and Prescott (1997), considers the existence of linear trends in
the time series as a special case. In the HP filtration technique, the potential component of output is obtained by
minimizing the following loss function:
∑( ) ∑( )
where S indicates the magnitude of the sample size, and λ expresses the weight of the potential output growth.
Changing this weight influences how potential output reacts to changes in actual output. According to the equation,
as the weight approaches infinity, the loss function is minimized by reducing the variations in the potential output.
This means that the potential output growth remains constant, in other words, the linear trend growth rate is
5. International Journal of Economics and Financial Research
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achieved. On the other hand, when the weight determined by the λ parameter is zero, the loss function is minimized
by abolishing the difference between potential and actual outputs. This means that the potential output is equal to
the actual output. This can be seen in Figure 6 below. When the values of λ are defined as 6000, 1600 and 100,
according to the figure, the smaller the parameter value, the smaller the estimated output gap values we get.
Figure-6. Prediction of Output Gap for Various λ Values
The most important advantage of the HP filtering technique is that a wide range of values for λ can make output
gap estimations stationary. On the other hand, this method also allows the trend to change over time. Thus, the
forecasting power is increased in the estimation of the output gap. What can be criticized in HP filtering technique is
that the value to be set for the λ parameter can be changed arbitrarily. For example, in Figure 6, considering the
estimation for the last quarter of 2002, output is over the potential at low weight (positive output gap), and below the
potential at high weight (negative output gap). In fact, in different estimates we make, when the λ value is
determined in the range of 100 - 1500, the output gap is positive, and when the λ value is over 1500, the output gap
is negative. Therefore, this method is not a useful method for determining the absolute value of an output gap at a
certain date.
With the HP filtering technique, λ value does not only influence the size of the gap, but it also influences the
relative value of the gap, and the timing of the troughs and peaks observed in output. For example, according to
Figure 6, the high λ value in 2007 points to a very high positive output gap rate compared to 2004, while the low λ
value indicates that this positive output gap rate cannot change much compared to 2004. However, this situation is
completely reversed in 2008. In this case, it can be said that the turning points in the output change with the value
determined for the weight (λ).
If the selection of the weight parameter is decisive in terms of the results, there should be a distinct and clear
criterion for the selection of the value of the parameter so that the method can be useful. According to the criterion
set by Hodrick and Prescott (1997), the appropriate λ value determines the relative magnitude of the variances of
shocks occurring in the temporary and permanent components of output. In the study mentioned, this value is
determined as 1600 for the real GDP time series for the U.S. Guay and St. Amant (1996) present Monte Carlo
evidence for the λ parameter, based on the frequency of the data, to be determined as 100 for the annual data, 1600
for the quarterly data and 14400 for the monthly data. For this reason, many empirical studies use these suggested
values for the HP filtering technique, taking the frequency of the data into account. While the performance of the
output gap estimates is evaluated in the following sections of our paper, the output gap values produced by the
proposed value of λ = 1600 for the quarterly frequency data will be taken as the output gap values generated by the
HP filtering method.
3.2. Estimation of Output Gap by Structural Techniques
Structural methods for output gap estimation can be considered as the methods based on the theory of
economics. As can be seen above, the linear trend and HP filtering techniques we have discussed are pure statistical
techniques. Other numerical and structural information that can be obtained from economics is not used in the
application of these methods. The output values for quarterly frequency are enough to estimate it. In the case of
structural forecasting methods, the estimated potential output is influenced by possible economic factors. We will
use the two most common methods in the literature for this purpose. These methods are multivariate structural
method and structural vector autoregressive model method.
3.2.1. Multivariate Structural Model
It is possible to mention some economic indicators (such as capacity utilization rate, electricity consumption)
and economic relations (such as the Phillips Curve and the Okun Law) that contain information about the supply side
-.10
-.08
-.06
-.04
-.02
.00
.02
.04
.06
02 03 04 05 06 07 08 09 10 11 12 13 14
GAP100 GAP1600 GAP6000
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of the economy and the business cycle conditions. As a matter of fact, Laxton and Tetlow (1992) enchance the HP
filtering technique to cover economic information in order to estimate output gap. Accordingly, potential output is
defined as a time series that minimizes the loss function as follow:
∑( ) ∑( ) ∑ ∑ ∑
where, in addition to the variables previously defined ε represents the error terms obtained from regression
equations. The terms π, u, and cu in the subscripts of the error terms refer to the Phillips Curve Equation, the Okun’s
Law Equation, and the Capacity Utilization Equation, respectively. Therefore, the corresponding error term is used
in the loss function of the error terms obtained by estimating these equations. On the other hand, the parameters μ, β
and φ in the loss function should be considered as time varying weights. In deriving these error terms, the following
equations are used:
Phillips Curve: ( )( )
Okun’s Law: ( )( )
Capacity Utilization: ( )( )
When actual output is larger than potential output (assuming a positive output gap), according to Phillips Curve,
the realized inflation will be higher than the anticipated inflation. According to Okun’s Law equation, actual
unemployment rate is lower than NAIRU value, i.e. unemployment rate which does not accelerate inflation
(equilibrium unemployment rate), when actual output is higher than potential output. According to the equation of
capacity utilization, which is regarded as an indicator of the supply side of the economy, the capacity utilization in
the economy is above the trend when the actual output is greater than the potential output. The behavior of the
variables involved in these equations are shown in Figures 7 8 and 9.
Figure-7. Annual Inflation Rate (2002 - 2014)
Figure-8. Unemployment Rate and NAIRU (2002 - 2014)
.0
.1
.2
.3
.4
.5
.6
.7
.8
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.07
.08
.09
.10
.11
.12
.13
.14
.15
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Figure-9. Capacity Utilization Rate and Its Average (2002 - 2014)
The multivariate model included in the equations above allows to determine the following items:
(1) the weighted average of actual output deviations from potential output,
(2) changes in the rate of increase in potential output, and
(3) the potential level of output by minimizing the errors caused by the three predetermined structural
relations (Phillips Curve, Okun’s Law and capacity utilization).
The determination of the output gap based on the information listed above allows for more realistic estimates of
potential output. It is also expected that the output gap values obtained will become more reliable.
In order to estimate the output gap by using this method, it is necessary to estimate the Phillips Curve, the
Okun’s Law and the capacity utilization equations. To obtain the initial values in these equations, potential output is
based on the HP filtering technique and λ = 1600. Another problem in the estimation of the Phillips curve is the
formation of inflation expectations. We will assume that the expectations are adaptive and determined by the past
inflation rates. Since the model is estimated by using quarterly frequency data, taking the annual inflation rates in the
past year as a basis can be considered as a sufficient in the formation of expectations. In the light of these
explanations, the estimated Phillips Curve equation is as follow:
πt = 0,32 πt-1 + 0,11 πt-2 + 0,07 πt-3 + 0,36 πt-4 + 0,11(yt – yt
T
)
(0,11) (0,12) (0,09) (0,09) (0,09)
RSS = 0,01385
The values given in parentheses below the coefficient estimates in the above equation represent the standard
error of the coefficient estimated and RSS represents the residual sum of squares. Since this equation will be used in
forecasting potential output, the result shows that current inflation should be considered in estimating the current
output gap.
The NAIRU value, in the the Okun’s Law equation has been determined by using the long-term trend value as
indicated by Debelle and Vickery (1997). Figure 8 which was given earlier shows the course of this long-term value.
Accordingly, the estimate for the Okun’s Law equation is as follow:
(ut – nairut) = -0,32 (yt – yt
T
)
(0,0,5)
RSS = 0,004734
According to this equation, unemployment will decrease if the current demand in the economy is strong
compared to potential output. On the contrary, if there is a negative output gap, there will be an upward trend in
unemployment.
Capacity utilization included in the model as an indicator of the supply side of the economy is the
manufacturing industry capacity utilization rate determined by the questionnaire of the Turkish Statistical Institute.
The trend of these data and the comparison of them with the whole period average can be seen in Figure 9. First of
all, it is seen that 2008 - 2009 global crisis caused a serious deviation in capacity utilization rate. Given that the
capacity utilization rate, which declined to 60 per cent at the beginning of 2009, was about 74.5 per cent of the
survey period average, the size of the slip emerges. On the other hand, the creation of these data through the
questionnaire method also implies a number of problems. For example, whether firms make a clear distinction about
labor and capital constraints, or whether the conditions that firms define as "normal" vary depending on the situation
in the business cycle are two of these problems. Despite these disadvantages, the most important data we can use for
the supply side of the economy is the capacity utilization rate, and it is among the leading indicators of the business
cycle. The estimated equation for capacity utilization in this study is as follow:
cut = -0,01 + 0,90 (yt – yt
T
)
(0,003) (0,12)
RSS = 0,028200
.60
.64
.68
.72
.76
.80
.84
02 03 04 05 06 07 08 09 10 11 12 13 14
CU CUA
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This equation fits the theoretical expectations indicating that capacity utilization carries information that should
be considered about the current output gap in the economy. Residual terms obtained from the above equations can be
seen in Figure 10 below.
Figure-10. Residuals related to structural equations
By using these residual series, the previously given multivariate loss function can be minimized for solving
potential output. The basic properties of the successive minimization technique used can be explained as follow:
First, residual terms obtained by predicting the above structural equations will form the basic variables to be used in
estimating the loss function. By using these terms in the first stage, the potential output value is estimated, and the
initial output gap is calculated. Then structural equilibrium is re-estimated by including output gap, so that the
variability of potential output is minimized. This procedure continues as the coefficient for the output gap variable
decreases, and when the coefficient increases, the previous regression equation is considered to be the equation that
fulfills the minimization condition. The potential output values obtained from the estimation of this last equation are
the basic values used in the calculation of the output gap. Given the number of explanatory variables used and the
size of the data set, the methodology described above requires the estimation of 74 consecutive regression equations
and in the 73th estimation minimization condition is provided. The Figure 11 below shows the potential output
values obtained from the last equation providing the minimization condition and the calculated output gap values
accordingly.
Figure-11. Multivariate Structural Model Potential Output and Output Gap Predictions
Compared to the previously calculated output gap values, it is seen that there is a serious contraction in the
output gap values calculated with this method. The averages of the output trend estimates for the linear trend, and
HP filtering techniques that were performed before are 0.001317 and 0.000129, respectively. The average of the
output gap values obtained from the multivariate structural model is very close to zero (-8,34E-14). On the other
hand, the standard deviations of the output gap values produced by linear trend and HP filtering methods are
-.12
-.10
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-.04
-.02
.00
.02
.04
.06
02 03 04 05 06 07 08 09 10 11 12 13 14
RESPHILLIPS RESOKUN RESCUEQ
18.0
18.1
18.2
18.3
18.4
18.5
18.6
18.7
18.8
-.004
-.003
-.002
-.001
.000
.001
.002
.003
.004
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9. International Journal of Economics and Financial Research
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0.041653 and 0.027280, respectively, whereas the standard deviation value obtained from the multivariate structural
model is 0.001993. In this case, it can be said that the use of additional economic information makes it more realistic
to estimate the output gap. Moreover, the output gap values support the results obtained by the Phillips Curve and
Okun’s Law equations.
3.2.2. Structural Vector Auto-Regression (SVAR) Model
In this part of the study, the potential output and output gap values will be estimated by using the three-variable
structural vector autoregressive (SVAR) model proposed by Bjørnland et al. (2006). It may be considered as a
necessity to incorporate the long-term constraints proposed by Blanchard and Quah (1989), which are the main
features of the SVAR model, into the model. The mentioned authors apply the long-run constraints to the bivariate
VAR model to see the consequences of long-lasting permanent shocks and short-term transitory shocks. If the GDP
time series data that is used to represent output have a high frequency (for instance, quarterly data like in our
analysis), the short-term is accepted as a period through which amount of production factors, consumption habits and
productivity are constant; short term or temporary shocks are assumed as stemming from the demand side of the
economy. On the other hand, in the long run, the quantity of production factors, habits, expectations, efficiency and
technology are considered to be dynamic. In this case, permanent or long-term shocks must be accepted as
originating from the supply side of the economy.
The starting point of the model is the ordering of the variables in the three-variable VAR model and the
inclusion of constraints into the model. In the Cholesky decomposition framework, variables included in the model
are unemployment rate, real GDP, and the inflation rate measured by consumer price index. The presentation of the
model and the constraints to be applied are as follow:
∑ ( ) ∑ ( ) ∑ ( )
∑ ( ) ∑ ( ) ∑ ( )
∑ ( ) ∑ ( ) ∑ ( )
In other words, it can be written as;
[ ] [
( ) ( ) ( )
( ) ( ) ( )
( ) ( ) ( )
] [ ] [ ]
In the above notation [ψu, ψy, ψπ] refers to the deterministic trend vector, and Aij(L) is the lag operator. Lag level
is determined by the formula E(εtεt’) = I. Since shocks cannot be observed, the VAR model should be estimated in
the following form:
[ ] [
( ) ( ) ( )
( ) ( ) ( )
( ) ( ) ( )
] [ ] [ ] [ ]
Correspondingly, the residual terms from the VAR model can be written as:
[ ] [ ] [
( ) ( ) ( )
( ) ( ) ( )
( ) ( ) ( )
] [ ]
Considering the last obtained model, while A(0) describes the simultaneous effects of shocks in the system, ε1
shows the permanent (aggregate supply) shocks, ε2 shows the real demand (aggregate demand) shocks and ε3 shows
the nominal demand (inflation) shocks in the [ε1, ε2, ε3] vector. In this system of equations, the SVAR model is
estimated by imposing the long-term restriction Hij(L)=0 for i = 1,2,3.
Table-2. SVAR Model Forecast Results
Δut Δyt πt
Δut-1 -0,181
(0,144)
-0,025
(0,086)
-0,025
(0,227)
Δyt-1 -0,085
(0,128)
0,806
(0,076)
1,013
(0,201)
πt-1 -0,001
(0,012)
0,015
(0,007)
0,759
(0,019)
RSS = 0,00805
Log Likelihood =0,457
Akiake Criteria = -17,60
Impulse response functions obtained from the SVAR model produce results that are consistent with the
economic theory as seen Figure 12 below. For instance, production gives a serious negative response to a positive
shock in unemployment and this effect has a lasting influence in ten quarters. On the other hand, production shows
10. International Journal of Economics and Financial Research
247
gradually negative response to a positive shock in inflation from the begging of the second quarter and this effect has
a lasting influence in ten quarters. The output gap values calculated according to the potential output estimations
obtained from the above model can be seen Figure 13:
.
Figure-12. SVAR Model Impulse-Response Functions
Figure-13. SVAR Model Output Gap Predictions
Except for negative values for 2009, the output gap values obtained in this model are often positive at varying
scales. It we look at the periodic developments in Turkish economy that we shortly discussed in the first part of the
study and consider the statistical properties of potential output, we can conclude that the SVAR model produces
more realistic output gap estimations than other models.
4. Evaluation of Output Gap Predictions
In this section, we will compare the models which we have used up to now for predicted output gap. We will
also make a general and empirical evaluation of the following models: linear trend models, the Hodrick-Prescott
filtering, multivariate structural equation and three-variable SVAR models. Figure 14 below shows the potential
output estimates obtained from the models, and Figure 16 shows the output gap values defined as the difference
between the actual and the potential output values.
-.0050
-.0025
.0000
.0025
.0050
.0075
.0100
1 2 3 4 5 6 7 8 9 10
Response of DUN to DUN
-.0050
-.0025
.0000
.0025
.0050
.0075
.0100
1 2 3 4 5 6 7 8 9 10
Response of DUN to DALY
-.0050
-.0025
.0000
.0025
.0050
.0075
.0100
1 2 3 4 5 6 7 8 9 10
Response of DUN to INF
-.012
-.008
-.004
.000
.004
.008
1 2 3 4 5 6 7 8 9 10
Response of DALY to DUN
-.012
-.008
-.004
.000
.004
.008
1 2 3 4 5 6 7 8 9 10
Response of DALY to DALY
-.012
-.008
-.004
.000
.004
.008
1 2 3 4 5 6 7 8 9 10
Response of DALY to INF
-.010
-.005
.000
.005
.010
.015
.020
1 2 3 4 5 6 7 8 9 10
Response of INF to DUN
-.010
-.005
.000
.005
.010
.015
.020
1 2 3 4 5 6 7 8 9 10
Response of INF to DALY
-.010
-.005
.000
.005
.010
.015
.020
1 2 3 4 5 6 7 8 9 10
Response of INF to INF
Response to Cholesky One S.D. Innovations±2 S.E.
-.04
-.02
.00
.02
.04
.06
.08
.10
.12
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Figure-14. Potential Output Estimates
Figure-15. Output Gap Estimates
The first issue in output gap estimations is that the estimates for each quarterly period are estimated within a
certain range, which is quite large from time to time. This naturally results in contradictory outcomes on the idle
capacity in the economy. These contradictions are more crucial when the estimation result of multivariate structural
model is compared with the results of other models. For instance, all the models used in this study produce negative
output gap estimates during 2008 global financial crisis. However, the multivariate structural model starts negative
values in the last quarter of 2006 while other models predict the starting time of negative gap values as the last
quarter of 2008. In other words, during the period of 2007-2008, the multivariate structural model shows negative
output gap values, while linear trend, HP filtering and SVAR models show positive output gap values. The same
situation applies to the last estimation period. While the three techniques listed above show a negative output gap at
the end of 2014, the multivariate structural model now gives a positive output gap value. It is possible to increase the
number of such contradictory periods. However, it is also necessary to emphasize a common characteristic of
estimated output gap values. Accordingly, the output gap estimates present similar characteristics. This can be
observed in Table 3, which shows the correlation coefficients among the respective output gap values.
Table-3. Output Gap Correlation Table
Gap Trend Gap HP Gap Structural Gap SVAR
Gap Trend 1,000
--
Gap HP 0,852
(0,000)
1,000
--
Gap
Structural
-0,239
(0,092)
0,056
(0,696)
1,000
--
Gap SVAR 0,976
(0,000)
0,800
(0,000)
-0,284
(0,043)
1,000
--
18.0
18.1
18.2
18.3
18.4
18.5
18.6
18.7
02 03 04 05 06 07 08 09 10 11 12 13 14
LAYFTREND LAYFHP1600
LAYFSTRUC LAYFSVAR
-.12
-.08
-.04
.00
.04
.08
.12
.16
.20
-.004
-.003
-.002
-.001
.000
.001
.002
.003
.004
02 03 04 05 06 07 08 09 10 11 12 13 14
GAPTREND GAP1600
GAPSTRUC GAPSVAR
12. International Journal of Economics and Financial Research
249
According to the table, the other models except the multivariate structural model have valid correlations as
statistical values ranging from 0.98 to 0.80. This shows that the output gap values obtained from the linear trend, HP
filtering and SVAR models are substantially similar. The correlation between the output gap values obtained from
the multivariate structural model and the values obtained with other methods is often inversed and weak. On the
other hand, inverse correlation coefficients according to the standard error values in parentheses below the
correlation coefficients are not statistically valid.
Besides the drawbacks of various output gap models mentioned above, the estimation results obtained vary
according to the estimation method used. This makes it difficult for us to assess the usefulness of estimation
methods, since their outcomes sometimes implicate serious contradictions with each other. Hence, an empirical
choice criterion is necessary to determine the usefulness of such prediction models. Unfortunately, there is no
empirical criterion developed in the context of output gap in the literature. For this reason, we will also prefer to take
advantage of the economic theory using the generally preferred method. Theoretically, the output gap has a
significant contribution in explaining the inflation in the country. Positive output gap values mark the periods when
inflationary pressures increase, and negative output gap values should be regarded as periods when inflationary
pressures are alleviated. For this reason, the output gap values that we have estimated using various methods so far
can be evaluated according to their strengths or contributions to explain this theoretical situation. The inflation
model which we use for this purpose is a basic mark-up inflation model. The following "gap" variant of this model,
given in the basic form below, requires the use of output gap values which we have previously estimated through
various methods:
In this equation, p shows consumer price index; ulc is the index value of unit labor cost, ip is the import price
index, gap is the output gap value and ξ is the error term. The lower case, notation as before, indicates that the
corresponding variable is logarithmically included in the model. Note that, in the mark-up inflation model expressed
in the error correction form, the lag structure of p, ulc and ip variables are definite but there is no information about
the lag structure in the gap variable. The lag structure of this variable will be determined separately for each gap
variable using the Akaike Information Criteria. In order to see the contribution of gap variable to the model, a model
in which output gap is not included and another model in which realized economic growth rates are included instead
of output gap growth rates will be estimated. Thus, the performance comparison can be made. The estimation results
are summarized in Table 4.
Table-4. Mark-Up Inflation Models and Impacts of the Output Gap
Coefficient No Output
Gap
Realized
Growth
Model
Linear
Trend
HP Filter Structural
Equation
SVAR
α0 4,86
(0,77)
4,85
(0,73)
3,60
(1,20)
3,54
(0,96)
6,23
(0,78)
3,34
(1,16)
-α1 -1,07
(0,19)
-1,04
(0,18)
-0,95
(0,21)
-1,18
(0,18)
-1,11
(0,25)
-0,94
(0,20)
α2 0,16
(0,03)
0,15
(0,04)
0,12
(0,05)
0,22
(0,04)
0,16
(0,04)
0,10
(0,05)
α3 0,16
(0,12)
0,17
(0,12)
0,05
(0,20)
0,50
(0,17)
0,02
(0,12)
0,11
(0,20)
α4 -0,57
(0,22)
-0,69
(0,22)
-0,50
(0,23)
-0,43
(0,21)
-0,10
(0,21)
-0,44
(0,23)
α5 -- 0,49
(0,20)
0,56
(0,41)
1,35
(0,51)
1,79
(0,68)
0,76
(0,43)
Adj R2
0,34 0,68 0,75 0,78 0,77 0,89
Q(12) 0,01 0,51 0,38 0,43 0,67 0,73
Note: Adj R2
is the adjusted coefficient of determination, Q (12) shows the level of significance
of the Box-Pierce test which proves that the autocorrelation function is zero. The values in
parentheses are the standard errors of the corresponding estimations.
In the analysis of the table, the first issue to remark is that the inflation model without output gap variable lacks
the explanatory power. This situation is a natural result of an inflation process which is mostly in the direction of
decline during the examination period and it indicates the existence of a permanent inflation problem. However, if a
measure of output gap is included in the inflation model, the explanatory power of the model rises significantly. Note
that by incorporating such a measure to the model, the problem of serial correlation in residuals no longer exists. On
the other hand, regardless of the estimation method, all models produce better results than the realized growth rates
model. This indicates the fact that the output gap is an important variable that must be included in the inflation
equation. In this context, output gap estimates obtained with SVAR model describes inflation most adequately.
Regarding the slope coefficients and the constant terms of equations, it can be said that whichever way the
output gap is estimated, the changes observed over time in the output gap can seriously help to determine the
inflation. We can explain the reason of this in two different ways. First, the inflationary effect caused by output gap
in the empirical inflation models is determined by the average value of the output gap, not by whether the output gap
is zero or not. This is similar to what we have done in this study. Estimated output gap values have different average
13. International Journal of Economics and Financial Research
250
values. So, the constant terms in each estimated inflation equation has different levels. Since the constant term
obtained through which the output gap series obtained by using multivariate structural model is highly different from
the other models constant terms, output gap values produced by this method can be regarded as unreliable estimates.
Second, in the empirical inflation models we have dealt with in this study, the effect of different business cycle
phases on inflation in the estimation of the output gap is balanced by the acquisition of different slope coefficients.
For example, the output gap estimate which produces a larger cycle, creates a smaller output gap coefficient in
inflation equation. For this reason, the predictive power of the equations are close even if the predicted parameters
change considerably.
Given that the aggregate demand is one of the fundamental sources of the changes in inflation, higher value of
forecast errors in the no-output gap equation indicates that inclusion of any gap measure to the inflation equation is
expected to reduce the forecast errors. Table 5 shows the out of sample forecasting performance of the various
methods used in this study for the last two years. The performance evaluation is made by using three basic criterias:
RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and Theil Inequality Coefficient (Theil’s U).
Table-5. Out of Sample Estimation Errors
Criteria No
Output
Gap
Growth
Model
Linear
Trend
HP Filter Structural
Equation
SVAR
RMSE 0,066 0,062 0,065 0,041 0,052 0,024
MAE 0,051 0,049 0,049 0,029 0,043 0,028
THEIL U 0,204 0,190 0,199 0,189 0,193 0,097
Biasness 0,000 0,000 0,000 0,000 0,000 0,000
Variance 0,100 0,086 0,095 0,084 0,123 0,002
Covariance 0,900 0,914 0,905 0,916 0,877 0,998
According to the table, SVAR model shows the best performance in estimating the inflation. Two models with
lowest RMSE values are SVAR and HP filtering techniques. The MAE value which indicates the deviation of the
predictions from the average in absolute terms is again the minimum for these two techniques. When Theil
inequality coefficient is taken as the basis, all models except SVAR model give approximate results, whereas SVAR
model gives the lowest value. On the other hand, when we look at the distribution of this error, it is understood that
the error percentage due to bias and variance is very close to zero, and that 99,8% of the errors are caused by
covariance. This represents a distribution very close to the ideal in the distribution of prediction errors. According to
the table, the most effective output gap values in describing and forecasting inflation are the output gap values
obtained from the SVAR model and HP filtering technique.
5. Conclusion
According to the output gap estimates, the estimates for each quarterly period were made within a certain range.
However, from time to time this range has expanded considerably. This situation naturally leads to contradictory
conclusions about the idle capacity in the economy. These contradictions increase the importance of the estimation
of the multivariate structural model that we have realized by theoretical relations and the fact that the output gap
values produced by the other three methods are very different from each other. The output gap estimates present
similar characteristics. Regarding the slope coefficients of the output gap estimates and the constant terms of the
equations, it can be said that whichever output gap estimate is considered, the changes observed over time in output
gap can seriously help in determining the inflation. We can explain the reason for this from two perspectives. First,
the inflationary effect caused by output gap in the empirical inflation models is determined by the average value of
the output gap, not by whether the output gap is zero or not. This is similar to what we have done in this study.
Estimated output gap values have different average values. So, the constant terms in each estimated inflation
equation has different levels. Since the constant term obtained through which the output gap series obtained by using
multivariate structural model is highly different from the other models, output gap values produced by this method
can be regarded as unreliable estimates. Second, in the empirical inflation models we have dealt with in this study,
the effect of different conjuncture phases on inflation in the estimation of the output gap is balanced by the
acquisition of different slope coefficients. For example, the output gap estimate which produces a larger cycle,
creates a smaller output gap coefficient in inflation equation. For this reason, the predictive power of the equations
are close even if the predicted parameters change considerably. According to the results obtained by diversifying the
mark-up inflation model, the SVAR model was chosen as the best model for comparing the forecast results and
guiding the inflation of the output gap calculated by different methods. The HP filter is the second best model. These
results were also supported by the measurement of out-of-sample forecast errors.
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