This document summarizes a study that analyzes the empirical relationship between exports and economic growth in India from 1972-1973 to 2010-2011 using time series econometric techniques. The study tests for causality and cointegration between GDP and exports. The results found GDP and exports are cointegrated, indicating a long-run equilibrium relationship. Granger causality testing confirmed bidirectional causality between GDP and exports. Error correction estimates also provided evidence of a short-run mutual causal relationship. In conclusion, the study finds support for export-led growth in India.
The long-run relationship between exports and imports has been the subject of intensive research in developed and developing economies. This relationship is of importance due to the fact that it reflects the stability of foreign trade situation of a country. The main objective of this paper is to study and investigate the long-run relationship between exports and imports in Ghana’s economy. A time series econometric techniques of unit root tests, Johansen cointegration and error-correction mechanism were applied. Annual data for real exports and real imports for the period 2002 – 2015 were used. The results of ADF unit root tests suggest that the two variables export and imports are integrated of order one. Johansen cointegration test revealed that, a long-run cointegrating relationship exist between exports and imports in Ghana. The error-correction model found a long-run unidirectional causality from imports to exports. This means that the short run fluctuations between exports and imports are sustainable since, in the long run, they will eventually converge towards an equilibrium state. The study confirms that Ghana is not in violation of its international budget constraints, and macroeconomic policies have been effective in bringing exports and imports into a long-run equilibrium.
This study is about the impact of selected macroeconomic variables on economic growth of Bangladesh. Economic growth of Bangladesh is measured in terms of annual nominal GDP growth rate. Least squared regression model has been employed considering exchange rate, export, import and inflation rate as independent variables and gross domestic product as the dependent variable in this study. The results reveal that export and import have significant positive impact on GDP growth rate. The other variables (exchange rate and inflation) are not significant, indicating that there exists no significant relationship among the variables. The findings will help the policy makers to make policies concerning the country’s economic growth to remain robust in the near future.
Import, Export and Economic Growth: the Case of Lower Income CountryIOSRJBM
Bangladesh is now considered as a lower middle-incomecountry by the blessing of international trade.Therefore, Bangladesh needs to take an effective policymaking decision in terms of international trade fortheirfurther development. Hence it is important to check whether Bangladesh needs more import or export for its further development. As a result, current research tries to see the relationship between import and GDP growth of Bangladesh by taking 32 years (1981-1992) of time series data.Relevant data were collected from the Bangladesh Bank website and World Bank Database. From the analysis, theresearcherconcludes that import is negatively related with GPD growth as well as GDP growth rate is also negatively related with Import.
Granger-causality Between Economic Growth and Sugar Exports in the Kingdom of...Premier Publishers
The main purpose of the study was to investigate the causal relationship between sugar exports and economic growth in the Kingdom of Eswatini using quarterly data covering the period from 2005 to 2017. Toda-Yamamota Granger-causality approach has been estimated using the bivariate Vector Autoregression (VAR) model that requires information about the optimal lag length as well as the maximum order of integration of the variables in the system in order to avoid spurious causality. The unit root test results showed that the variables are integrated of order one, I(1) using the Augmented Dickey-Fuller test. The optimal lag length for the variables in the system were selected to be two. The Granger-causality test results showed that sugar exports do Granger-cause economic growth but economic growth does not Granger-cause sugar exports meaning that there is a unidirectional causality from sugar exports to economic growth in the Kingdom of Eswatini. The study concludes by acknowledging that the country needs to develop its sugar exports in order to improve its economic growth. Therefore, policy makers should develop strategies that increases sugar export share by domestic firms in order to create a stronger national export sector.
The Relation Between Exports of Main Products And Economic Growth of Key Econ...inventionjournals
This paper clarifies the literature of key product export growth and regional economic growth. The paper analyses impacts of key product export on regional economic growth and vice versa. The paper provides recent empirical evidence of the relation. Besides an evaluation of the recent relation between export growth and economic growth in Viet Nam, the paper assesses the relation between key product export and economic growth during 1996-2012 period based on quantitative and qualitative approaches. With constructed models, the paper examines the relation between key product export and economic growth and concludes that it is positive. The research findings show that key product export in every economic region contributes positively to regional economic growth although it varies in different regions. Based on existing literature and empirical analysis, the paper provides a number of strategies to improve key product export contribution to key economic regions in the most effective manner and vice versa. The paper creates a fundament for researchers and policy makers both regionally and nationally in order for developing effective orientations, policies and measures for promoting export and sustainable eoconomic development.
This study aims to analyze the effect of foreign direct investment (FDI) on new job creation, and pays attention to factors interrelated to employment by using the case of Afghanistan. Using time series data form 2003 to 2017, this paper explore the driving forces and reduction potentials of employment in Afghanistan with consideration for dynamic changes within the traditional OLS and standardize OLS model. The results show that exchange rate plays a dominant role in increasing employment in Afghanistan. And exports and inflation rate plays a dominant role in decreasing employment in Afghanistan. All variables are co-integrated and the analysis of the impulse response function and variance decomposition turns out to be synchronous. Furthermore, in the short run export and inflation rate are more critical in reduction potentials of employment in Afghanistan. Policies should be advised to control inflation rate and illegal export and improve the investment projects to attract more FDI into the economy for quick adjustment purpose in case of the shock to the system.
This paper studies the causal relationship between inflation and economic growth in Qatar for the period of 1980 to 2016. A time series analysis of unit roots tests, Johansen cointegration method and Granger causality tests were applied on data. The variables were found to be cointegrated, hence a long run-relationship between them exists. Granger causality test found causality runs from inflation to economic growth.
The long-run relationship between exports and imports has been the subject of intensive research in developed and developing economies. This relationship is of importance due to the fact that it reflects the stability of foreign trade situation of a country. The main objective of this paper is to study and investigate the long-run relationship between exports and imports in Ghana’s economy. A time series econometric techniques of unit root tests, Johansen cointegration and error-correction mechanism were applied. Annual data for real exports and real imports for the period 2002 – 2015 were used. The results of ADF unit root tests suggest that the two variables export and imports are integrated of order one. Johansen cointegration test revealed that, a long-run cointegrating relationship exist between exports and imports in Ghana. The error-correction model found a long-run unidirectional causality from imports to exports. This means that the short run fluctuations between exports and imports are sustainable since, in the long run, they will eventually converge towards an equilibrium state. The study confirms that Ghana is not in violation of its international budget constraints, and macroeconomic policies have been effective in bringing exports and imports into a long-run equilibrium.
This study is about the impact of selected macroeconomic variables on economic growth of Bangladesh. Economic growth of Bangladesh is measured in terms of annual nominal GDP growth rate. Least squared regression model has been employed considering exchange rate, export, import and inflation rate as independent variables and gross domestic product as the dependent variable in this study. The results reveal that export and import have significant positive impact on GDP growth rate. The other variables (exchange rate and inflation) are not significant, indicating that there exists no significant relationship among the variables. The findings will help the policy makers to make policies concerning the country’s economic growth to remain robust in the near future.
Import, Export and Economic Growth: the Case of Lower Income CountryIOSRJBM
Bangladesh is now considered as a lower middle-incomecountry by the blessing of international trade.Therefore, Bangladesh needs to take an effective policymaking decision in terms of international trade fortheirfurther development. Hence it is important to check whether Bangladesh needs more import or export for its further development. As a result, current research tries to see the relationship between import and GDP growth of Bangladesh by taking 32 years (1981-1992) of time series data.Relevant data were collected from the Bangladesh Bank website and World Bank Database. From the analysis, theresearcherconcludes that import is negatively related with GPD growth as well as GDP growth rate is also negatively related with Import.
Granger-causality Between Economic Growth and Sugar Exports in the Kingdom of...Premier Publishers
The main purpose of the study was to investigate the causal relationship between sugar exports and economic growth in the Kingdom of Eswatini using quarterly data covering the period from 2005 to 2017. Toda-Yamamota Granger-causality approach has been estimated using the bivariate Vector Autoregression (VAR) model that requires information about the optimal lag length as well as the maximum order of integration of the variables in the system in order to avoid spurious causality. The unit root test results showed that the variables are integrated of order one, I(1) using the Augmented Dickey-Fuller test. The optimal lag length for the variables in the system were selected to be two. The Granger-causality test results showed that sugar exports do Granger-cause economic growth but economic growth does not Granger-cause sugar exports meaning that there is a unidirectional causality from sugar exports to economic growth in the Kingdom of Eswatini. The study concludes by acknowledging that the country needs to develop its sugar exports in order to improve its economic growth. Therefore, policy makers should develop strategies that increases sugar export share by domestic firms in order to create a stronger national export sector.
The Relation Between Exports of Main Products And Economic Growth of Key Econ...inventionjournals
This paper clarifies the literature of key product export growth and regional economic growth. The paper analyses impacts of key product export on regional economic growth and vice versa. The paper provides recent empirical evidence of the relation. Besides an evaluation of the recent relation between export growth and economic growth in Viet Nam, the paper assesses the relation between key product export and economic growth during 1996-2012 period based on quantitative and qualitative approaches. With constructed models, the paper examines the relation between key product export and economic growth and concludes that it is positive. The research findings show that key product export in every economic region contributes positively to regional economic growth although it varies in different regions. Based on existing literature and empirical analysis, the paper provides a number of strategies to improve key product export contribution to key economic regions in the most effective manner and vice versa. The paper creates a fundament for researchers and policy makers both regionally and nationally in order for developing effective orientations, policies and measures for promoting export and sustainable eoconomic development.
This study aims to analyze the effect of foreign direct investment (FDI) on new job creation, and pays attention to factors interrelated to employment by using the case of Afghanistan. Using time series data form 2003 to 2017, this paper explore the driving forces and reduction potentials of employment in Afghanistan with consideration for dynamic changes within the traditional OLS and standardize OLS model. The results show that exchange rate plays a dominant role in increasing employment in Afghanistan. And exports and inflation rate plays a dominant role in decreasing employment in Afghanistan. All variables are co-integrated and the analysis of the impulse response function and variance decomposition turns out to be synchronous. Furthermore, in the short run export and inflation rate are more critical in reduction potentials of employment in Afghanistan. Policies should be advised to control inflation rate and illegal export and improve the investment projects to attract more FDI into the economy for quick adjustment purpose in case of the shock to the system.
This paper studies the causal relationship between inflation and economic growth in Qatar for the period of 1980 to 2016. A time series analysis of unit roots tests, Johansen cointegration method and Granger causality tests were applied on data. The variables were found to be cointegrated, hence a long run-relationship between them exists. Granger causality test found causality runs from inflation to economic growth.
Inflation is a continual increase in general price level of goods and services in an economy over a period of time. It is caused by many factors, important among them are excess of demand of goods and services over supply, macroeconomic performance, money supply, economic policies implications, environmental factors etc. A number of researchers in the past made attempts to identify determinants of inflation and to investigate the impact of identified variables on inflation in European and also in some Asian economies. But, in context of India, not many studies can be traced in the literature. The purpose of this paper is to shed some light on the impact of selected variables on inflation in India. The paper considers CPI (Consumer Price Index) inflation as dependent variable and a set of independent macroeconomic variables, which includes Gross Domestic Product, Money Supply, Deposit Rate, Prime Lending Rate, Exchange Rate, Trade Volume (Value of Imports and Exports) and Crude Oil Prices. The empirical analysis covers the quarterly data series for ten financial years from 2002Q1 to 2012Q1. The collected data is analyzed using ADF Unit root test, Granger Causality test, and the Ordinary Least Square (OLS) technique.
EFFECTS OF FOREIGN TRADE ON AGRICULTURAL OUTPUT IN NIGERIA (1981-2018) IAEME Publication
The current study examined the impact of foreign trade on agricultural output in
Nigeria based on data sourced from 1981 to 2018 by employing a number of
estimation techniques such as Cobb-Douglas, unit root testing, autoregressive
distributed lag among others within the context of two profound theories of exchange
rate - the vent – for surplus theory of international trade; factor endowments theory.
Our study observed that foreign trade exerts negatively on agricultural output. Our
results have some empirical implications.
Asymmetric Co-integration between Exchange Rate and Trade Balance in ThailandPremier Publishers
This paper empirically examines the long-run exchange rate pass-through into trade balance in Thailand. The study incorporates political stability in the short run model to ascertain its effect on the trade balance. Asymmetric co-integrating adjustment method proposed by Enders and Siklos (2001) is employed for the study. The empirical findings revealed that there exists an asymmetric cointegration relationship between exchange rate and trade balance as well as exchange rate and imports & exports volumes after conducting a momentum-threshold autoregressive (M-TAR) and threshold autoregressive (TAR) tests respectively. The results of the short run effects showed that political stability has no meaningful effect on trade balance of Thailand. The findings have further shown that changes in real exchange rate have contributed to the presence of trade balance deficit in Thailand during the period under study; which is most likely to be as a result of massive imports of crude oil between the late 1990’s and through to 2010.
Effect of social capital on agribusiness diversification intention in the eme...Nghiên Cứu Định Lượng
This is the first study to explore the comprehensive effect of the facets of social capital on behavioral intention through behavioral goals and determinants of the TPB under the premises of the RBV. The findings will help emerging economies, for example, Vietnam, where most farmers are family business owners or microscaled entrepreneurs in agriculture.
This study examines whether shifts in exchange rate has symmetric or asymmetric impact on stock prices in Germany. Linear and nonlinear autoregressive distribution lag models are applied by using monthly data from the period January 1993 till April 2017. Findings suggest that only currency devaluation affects stock prices which implies asymmetric impact of changes in exchange rate on stock prices. The empirical results from this study would be useful for policymaking as well as for forecasting the impact of exchange rate changes on stock prices.
Inflation is a continual increase in general price level of goods and services in an economy over a period of time. It is caused by many factors, important among them are excess of demand of goods and services over supply, macroeconomic performance, money supply, economic policies implications, environmental factors etc. A number of researchers in the past made attempts to identify determinants of inflation and to investigate the impact of identified variables on inflation in European and also in some Asian economies. But, in context of India, not many studies can be traced in the literature. The purpose of this paper is to shed some light on the impact of selected variables on inflation in India. The paper considers CPI (Consumer Price Index) inflation as dependent variable and a set of independent macroeconomic variables, which includes Gross Domestic Product, Money Supply, Deposit Rate, Prime Lending Rate, Exchange Rate, Trade Volume (Value of Imports and Exports) and Crude Oil Prices. The empirical analysis covers the quarterly data series for ten financial years from 2002Q1 to 2012Q1. The collected data is analyzed using ADF Unit root test, Granger Causality test, and the Ordinary Least Square (OLS) technique.
EFFECTS OF FOREIGN TRADE ON AGRICULTURAL OUTPUT IN NIGERIA (1981-2018) IAEME Publication
The current study examined the impact of foreign trade on agricultural output in
Nigeria based on data sourced from 1981 to 2018 by employing a number of
estimation techniques such as Cobb-Douglas, unit root testing, autoregressive
distributed lag among others within the context of two profound theories of exchange
rate - the vent – for surplus theory of international trade; factor endowments theory.
Our study observed that foreign trade exerts negatively on agricultural output. Our
results have some empirical implications.
Asymmetric Co-integration between Exchange Rate and Trade Balance in ThailandPremier Publishers
This paper empirically examines the long-run exchange rate pass-through into trade balance in Thailand. The study incorporates political stability in the short run model to ascertain its effect on the trade balance. Asymmetric co-integrating adjustment method proposed by Enders and Siklos (2001) is employed for the study. The empirical findings revealed that there exists an asymmetric cointegration relationship between exchange rate and trade balance as well as exchange rate and imports & exports volumes after conducting a momentum-threshold autoregressive (M-TAR) and threshold autoregressive (TAR) tests respectively. The results of the short run effects showed that political stability has no meaningful effect on trade balance of Thailand. The findings have further shown that changes in real exchange rate have contributed to the presence of trade balance deficit in Thailand during the period under study; which is most likely to be as a result of massive imports of crude oil between the late 1990’s and through to 2010.
Effect of social capital on agribusiness diversification intention in the eme...Nghiên Cứu Định Lượng
This is the first study to explore the comprehensive effect of the facets of social capital on behavioral intention through behavioral goals and determinants of the TPB under the premises of the RBV. The findings will help emerging economies, for example, Vietnam, where most farmers are family business owners or microscaled entrepreneurs in agriculture.
This study examines whether shifts in exchange rate has symmetric or asymmetric impact on stock prices in Germany. Linear and nonlinear autoregressive distribution lag models are applied by using monthly data from the period January 1993 till April 2017. Findings suggest that only currency devaluation affects stock prices which implies asymmetric impact of changes in exchange rate on stock prices. The empirical results from this study would be useful for policymaking as well as for forecasting the impact of exchange rate changes on stock prices.
This paper applies the Vector Autoregressive (VAR) technique to annual data from 1980 to 2013 to provide empirical evidence on the long-run relationship between export trade and economic growth in Malawi. The export trade in this study is disaggregated into services and goods exports. Thus, the paper estimated two models. The first model deals with the relationship between export of services and growth, and the other one determines the relationship between goods export and growth. While the paper finds no evidence for long-run relationship between export of services and goods on economic growth, the empirical results suggest existence of a short-run nexus between export of goods and economic growth in Malawi. The Granger causality test results have also confirmed existence of a unidirectional causality from goods exports to economic growth and another unidirectional causality from goods exports to service exports.
This paper applies the Vector Autoregressive (VAR) technique to annual data from 1980 to 2013 to provide empirical evidence on the long-run relationship between export trade and economic growth in Malawi. The export trade in this study is disaggregated into services and goods exports. Thus, the paper estimated two models. The first model deals with the relationship between export of services and growth, and the other one determines the relationship between goods export and growth. While the paper finds no evidence for long-run relationship between export of services and goods on economic growth, the empirical results suggest existence of a short-run nexus between export of goods and economic growth in Malawi. The Granger causality test results have also confirmed existence of a unidirectional causality from goods exports to economic growth and another unidirectional causality from goods exports to service exports.
Agricultural Export, Oil Export and Economic Growth in Nigeria: Multivariate ...Agriculture Journal IJOEAR
Abstract—Sustaining of nation’s economic growth for better footing and outlook is very crucial for the globe of recent, most especially for developing countries like Nigeria. The country as a vivid example of a developing nation is oil based economy, which adopts export promotion policy as the essentialtactic for growth. Yet the nation has not maximized her abundance of resources to aids growth, despite notable economic growth being experienced. In this view, there is an attempt to examine the relationship among agricultural export, oil export and output growth in Nigeria. The causal relationship among the variables was investigated by using times series data for the period between 1981 and 2014. All the macroeconomic variables were found to be stationary. The study revealed that there is significant relationship between economic growth and the agricultural export and oil export. Based on the findings, government of the country is being advised to initiate new and re-defined old policies that will diversify the export base. Likewise, policies that will improveand aid the nation’s domestic production is being encouraged, since long run relationship has been established among the macroeconomic variables.
This article seeks to examine the impact of the Bangladesh’s stock market development on its economic growth from the period of 1989-2012. We have used Johansen Cointegration test to estimate the long-run equilibrium relationship between the variables and the Granger causality test was conducted in order to establish causal relationship, while the model was estimated using the error correction model (ECM). Johansen co-integration test results show that the Bangladesh’s stock market development and economic growth are co-integrated. This indicates that a long run relationship exists between stock market development and economic growth in Bangladesh. The causality test results suggest a unidirectional causality from stock market development to the economic growth. On the other hand, there is no “reverse causation” from economic growth to stock market development. The evidence from this study reveals that the activities in the stock market tend to impact positively on the economy. It is recommended therefore that stock market regulatory authority should therefore address policy issues that are capable of boosting the investors’ confidence through improved policy formulation and creation of awareness.
An Empirical Study on the Relationship between Economic Openness and Economic...Editor IJCATR
Economic openness is the measure of economic activity in the country comprehensive index. How is economic openness indicator measured? Chinese economy has experienced rapid growth for more many years, what is on earth the effect of economic opening on Chinese economic growth? The answer to this question will provide instructive revelation about the selection of Chinese reasonable opening policy. Economic openness is measured by trade openness, foreign investment openness and financial openness in this paper. Based on Solow economic growth model and beginning with foreign trade, foreign investment and financial development, this paper made regression analysis using Chinese data from 1985 to 2004. The empirical analysis indicates that the domestic capital input is still the primary element that promotes Chinese economic growth, by contrast, the effect of foreign trade and foreign investment on Chinese economic growth is faint. Again, financial development on the impetus of economic growth in China has a room to rise.
Post Deregulation Evaluation of Non-Oil Export and Economic Growth Nexus in N...iosrjce
The impact of non-oil export on economic growth in Nigeria has been one of the most debated issues
in recent years. This study examines the role of non-oil export on economic growth since deregulation between
1986 when deregulation took effect and 2012 which previous studies might have ignored. In achieving the
objectives of the study, Ordinary Least Square Methods was employed. The study reveals that the impact of nonoil
export on the economic growth was significant and positive as a unit increase in non-oil export impacted
positively by 43% on the productive capacity of goods and services in Nigeria during the period. This is evident
in the study that the contribution of non-oil sector during the period in Nigeria has improved above the results
of other studies carried out from the pre-deregulation era. The study among other things encourages the
government to further reinforce the legislative and supervisory framework of the non-oil sectors in Nigeria and
diversify the economy to ensure utmost contributions from all faces of the non-sector to economic growth of Nigeria.
International Trade and Economic Growth: A Cointegration Analysis for UgandaPremier Publishers
The focus of the study was to establish whether there exists a long run relationship between various trade and other macro economic variables for Uganda for the period 1982 to 2018. The Autoregressive distributed lag (ARDL) model was used to establish the existence of a long run relationship between economic growth and trade variables. The empirical results suggest that in the short run, imports reduced development by -0.11 in second lag as P=0.025<0.005, while the exports increased development by 0.08 in the first lag as the p=0.015<0.005. But this was not true for the second lag. Lastly at all lags for the short run, inflation had positive run relationship on development (GDP). However, in the long run inflation reduced development by 0.61 ceteris-paribus at 5% level of significance.
Similar to 3.[24 38]sarbapriya ray final paper (20)
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
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Memorandum Of Association Constitution of Company.pptseri bangash
www.seribangash.com
A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
https://seribangash.com/article-of-association-is-legal-doc-of-company/
Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
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Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
https://seribangash.com/promotors-is-person-conceived-formation-company/
Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
Business Valuation Principles for EntrepreneursBen Wann
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
LA HUG - Video Testimonials with Chynna Morgan - June 2024Lital Barkan
Have you ever heard that user-generated content or video testimonials can take your brand to the next level? We will explore how you can effectively use video testimonials to leverage and boost your sales, content strategy, and increase your CRM data.🤯
We will dig deeper into:
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3. How you can capture more CRM data to understand your audience better through video testimonials. 📊
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
Improving profitability for small businessBen Wann
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3.[24 38]sarbapriya ray final paper
1. International Affairs and Global Strategy www.iiste.org
ISSN 2224-574X (Paper) ISSN 2224-8951 (Online)
Vol 1, 2011
A Causality Analysis on the Empirical Nexus between Export
and Economic Growth: Evidence from India
Sarbapriya Ray
Dept. of Commerce, Shyampur Siddheswari Mahavidyalaya,
University of Calcutta,West Bengal, India.
Tel:+91-33-9433180744,E-mail:sarbapriyaray@yahoo.com
Abstract
The paper tries to assess empirically the relationship between export and economic growth in India using
annual data over the period 1972-73 to 2010-11. Time-series econometric techniques (Granger causality
and cointegration) are applied to test the hypothesis of economic growth strategy led by exports. The paper
is based on the following hypotheses for testing the causality and co-integration between GDP and export
in India as to whether there is bi-directional causality between GDP growth and export, or whether there is
unidirectional causality between the two variables or whether there is no causality between GDP and export
in India or whether there exists a long run relationship between GDP and Export India. The cointegration
test confirmed that economic growth and exports are co integrated, indicating an existence of long run
equilibrium relationship between the two as confirmed by the Johansen cointegration test results. The
Granger causality test finally confirmed the presence of bi-directional causality which runs from economic
growth to export and vice-versa. The error correction estimates gave evidence that in short run also,
export and GDP are mutually causal.
Keywords: Exports, economic growth, India, causality, cointegration, error correction model.
1. Introduction:
The role of exports in economic performance of developing countries like India has become one of the more
popularly researched topics during post liberalization period. Exports are the most significant source of
foreign exchange, which can be used to ease pressure on the balance of payments and generate much-needed
job opportunities. Exports can help the country to integrate in the world economy and help to reduce the
impact of external shocks on the domestic economy. Exports allow domestic production to achieve a high
level of economies of scale. The major momentum for most studies on this relationship is the export-led
growth hypothesis which fascinatingly represents a leading explanation in this context. The issue of how a
country can achieve economic growth is one of the fundamental economic questions. An export-led growth
hypothesis (Balassa 1978, Bahagwati 1978, Edwards 1998) states that exports are means to promoting
economic growth. The export development and free entry and exit are considered as the key causes of
economic growth. Firms can take advantage of more efficient allocation of resources, scale economies and
encouraging creativity and innovation caused by foreign competition (Helpman and Krugman, 1985).
Moreover, export can cause more import of intermediate goods which leads to increase of capital
accumulation and output growth. Therefore, the export-led growth hypothesis states that the growth of
exports has a favorable impact on economic growth. However, the empirical evidence on the causal
relationship between exports and growth is diverse. There is a substantial literature that investigates the
relationship and causation between exports and economic growth, but the conclusions still remain a subject
of debate. In particular, available time series studies fail to provide consistent support for the export-led
growth hypothesis while most cross-sectional studies provide empirical evidence in support of the
hypothesis.
It is generally customary that countries which display glaring picture in their export performance also
replicate healthy sign in their GDP performance and vice versa. This moves up a vital question as to the
nature of the association between the two. It also raises an interesting issue on whether there is a causal
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nexus between export and economic growth via GDP growth.
In view of the above analysis, the paper attempts to evaluate the direction of causality between export
and economic growth in India during 1972-73 to 2010-11 covering a period of 39 years.
The paper is organised as follows: Section 2 briefly presents the review of existing literature, section 3
discusses the methodology and data base; section 4 analyses the empirical results, while section 5 presents
summary and conclusion.
2.Literature Review:
Darrat (1986) studied on four Asian (Hong Kong, South Korea, Singapore, and Taiwan) countries and
observed no evidence of uni-directional causality from exports to economic growth in all the four
economies. In the case of Taiwan, however, the study identified unidirectional causality from economic
growth to export growth.
Sinha (1999) examined the relationship between export instability, investment and economic growth in
Asian countries using time series data and the cointegration methodology framework. The study found that
most of the variables are non-stationary in their levels and not cointegrated. For Japan, Malaysia,
Philippines and Sri Lanka, the study found a negative relationship between export instability and economic
growth but for (South) Korea, Myanmar, Pakistan and Thailand, the study founds a positive relationship
between the two variables. For India, it was found to be mixed results. In most cases, economic growth was
found to be positively associated with domestic investment.
Erfani (1999) examined the causal relationship between economic performance and exports over the
period of 1965 to 1995 for several developing countries in Asia and Latin America. The result showed the
significant positive relationship between export and economic growth. This study also provides the
evidence about the hypothesis that exports lead to higher output.
Vohra (2001) tested the relationship between the export and growth in India, Pakistan, the Philippines,
Malaysia, and Thailand for 1973 to 1993. The empirical results indicated that when a country has achieved
some level of economic development than the exports have a positive and significant impact on economic
growth. The study also showed the importance of liberal market policies by pursuing export expansion
strategies and by attracting foreign investments.
Balaguer (2002) examined the hypothesis of export-led growth from the Spanish trade liberalization
process initiated four decades ago, for 1961 to 2000. Both the export expansion and the progression from
“traditional” exports to manufactured and semi-manufactured export is considered for this purpose. It is
proved that the structural transformation in export composition has become a key factor for Spain’s
economic development along with the relationship between export and real output.
Subasat (2002) investigated the empirical nexus between exports and economic growth which
suggested that the more export oriented countries like middle-income countries grow faster then the
relatively less export oriented countries .The study also showed that export promotion does not have any
significant impact on economic growth for low and high income countries.
Amavilah (2003) determined the role of exports in economic growth by analyzing Namibia’s data from
1968 to 1992. Results explained the general importance of exports, but find no discernible sign of
accelerated growth because of exports.
Lin (2003) stated that ten percent increase in exports cause one percent increase in GDP in the 1990s in
China on the basis of new proposed estimation method, when both direct and indirect contributions are
considered.
Shirazi (2004) studied the short run and long run relationship among real export, real import and
economic growth on the basis of co-integration and multivariate Granger causality developed by Toda and
Yamamoto (1995) for the period 1960 to 2003.This study showed a long-run relationship among import,
export and economic growth and found unidirectional causality from export to output while did not find
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any significant causality between import and export.
Mah (2005) studied the long-run causality between export and growth with the help of significance of
error correction term, ECt-1. This study also indicated that export expansion is insufficient to explain the
patterns of real economic growth.
Tang (2006) stated that there is no long run relationship among export, real Gross Domestic product and
imports. This study further shows no long-run and short-run causality between export expansion and
economic growth in China on the basis of Granger causality while economic growth does Granger-cause
imports in the short run.
Jordaan (2007) analyzed the causality between exports and GDP of Namibia for the period 1970 to
2005. The hypothesis of growth led by export is test through Granger causality and cointegration. It tests
whether there is uni-directional or bi-direction causality between export and GDP. The results revealed that
exports Granger cause GDP and GDP per capita and suggested that the export-led growth strategy through
various incentives has a positive influence on growth.
Pazim (2009) tested the validity of export-led growth hypothesis in three countries by using panel data
analysis. It is concluded that there is no significant relationship between the size on national income and
amount of export for these countries on the basis of one-way random effect model. The panel unit root test
shows that the process for both GDP and Export at first difference is not stationary while the panel
co-integration test indicates that there is no co-integration relationship between the export and economic
growth for these countries.
Ullah et al (2009) investigated Export-led-growth by time series econometric techniques (Unit root
test,Co-integration and Granger causality through Vector Error Correction Model) over the period of 1970
to 2008 for Pakistan. In this paper, the results reveal that export expansion leads to economic growth. They
also checked whether there is uni-directional or bidirectional causality between economic growth, real
exports, real imports, real gross fixed capital formation and real per capita income. The traditional Granger
causality test suggests that there is uni-directional causality between economic growth, exports and imports.
On the other hand Granger causality through vector error correction was checked with the help of F-value
of the model and t-value of the error correction term, which partially reconciles the traditional Granger
causality test.
3. Methodology:
3.1. Data and Variables
The objective of this paper is to investigate the dynamics of the relationship between export and economic
growth in India using the annual data for the period, 1972-73 to 2010-11 which includes the 39 annual
observations. The two main variables of this study are economic growth and export. The real Gross
Domestic Product (GDP) is used as the proxy for economic growth in India and we represent the economic
growth rate by using the constant value of Gross Domestic Product (GDP) measured in Indian rupee. All
necessary data for the sample period are obtained from the Handbook of Statistics on Indian Economy,
2010-11 published by Reserve Bank of India. All the variables are taken in their natural logarithms to avoid
the problems of heteroscedasticity.
Using the time period, 1972-73 to 2010-11 for India, this study aims to examine the long-term and causal
dynamic relationships between the level of export and economic growth. The estimation methodology
employed in this study is the cointegration and error correction modeling technique.
The entire estimation procedure consists of three steps: first, unit root test; second, cointegration test; third,
the error correction model estimation.
3.2. Econometric specification:
3.2.1Hypothesis:
The paper is based on the following hypotheses for testing the causality and co-integration between GDP
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and export in India (i) whether there is bi-directional causality between GDP growth and export, (ii)
whether there is unidirectional causality between the two variables, (iii) whether there is no causality
between GDP and export in India (iv) whether there exists a long run relationship between GDP and EX in
India.
3.2.2.Model Specification
The choice of the existing model is based on the fact that it allows for generation and estimation of all the
parameters without resulting into unnecessary data mining.
The growth model for the study takes the form: GDP=f (EX) -------------------(1)
Where GDP and EX are the gross domestic product and export respectively.
Equation (1) is treated as a Cobb-Douglas function with export from India, (EX), as the only explanatory
variable.
The link between Economic growth (measured in terms of GDP growth) and export in India can be
described using the following model in linear form:
LnGDPt= α + βLn EX t + εt -------------- (1.1)
α and β>0
The variables remain as previously defined with the exception of being in their natural log form. εt is
the error term assumed to be normally, identically and independently distributed.
Here, GDP t and EX t show the Gross Domestic Product annual growth rate and export growth at a
particular time respectively while εt represents the “noise” or error term; α and β represent the slope and
coefficient of regression. The coefficient of regression, β indicates how a unit change in the independent
variable (export) affects the dependent variable (gross domestic product). The error, εt, is incorporated in
the equation to cater for other factors that may influence GDP. The validity or strength of the Ordinary
Least Squares method depends on the accuracy of assumptions. In this study, the Gauss-Markov
assumptions are used and they include; that the dependent and independent variables (GDP and EX) are
linearly co-related, the estimators (α, β) are unbiased with an expected value of zero i.e., E (εt) = 0, which
implies that on average the errors cancel out each other. The procedure involves specifying the dependent
and independent variables; in this case, GDP is the dependent variable while EX the independent variable.
But it depends on the assumptions that the results of the methods can be adversely affected by outliers.
In addition, whereas the Ordinary Least squares regression analysis can establish the dependence of either
GDP on EX or vice versa; this does not necessarily imply direction of causation. Stuart Kendal noted that
“a statistical relationship, however, strong and however suggestive, can never establish causal connection.”
Thus, in this study, another method, the Granger causality test, is used to further test for the direction of
causality.
Step –I: Ordinary least square method:
Here we will assume the hypothesis that there is no relationship between export (EX) and Economic
Growth in terms of GDP. To confirm about our hypothesis, primarily, we have studied the effect of foreign
trade on economic growth and vice versa by two simple regression equations:
EXi=a+ b*GDPi ……………………………………………………………………………………………………………..(2)
GDPi=a1+ b1*EXi……………………………………………………………………………………………………………………………(3)
GDP = Gross domestic product.
EX = Export from India.
t= time subscript.
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This study aimed to examine the long-term relationship between export and GDP growth in India between
1972-72 and 2010-11. Using co-integration and Vector Error Correction Model (VECM) procedures, we
investigated the relationship between these two variables. The likely short-term properties of the
relationship among economic growth and export were obtained from the VECM application. Next, unit root,
VAR, cointegration and Vector Error Correction Model (VECM) procedures were utilized in turn. The first
step for an appropriate analysis is to determine if the data series are stationary or not. Time series data
generally tend to be non-stationary, and thus they suffer from unit roots. Due to the non-stationarity,
regressions with time series data are very likely to result in spurious results. The problems stemming from
spurious regression have been described by Granger and Newbold (1974). In order to ensure the condition
of stationarity, a series ought to be integrated to the order of 0 [I(0)]. In this study, tests of stationarity,
commonly known as unit root tests, were adopted from Dickey and Fuller (1979, 1981) and Phillips-Perron
test. As the data were analyzed, we discovered that error terms had been correlated in the time series data
used in this study.
Step –II: The Stationarity Test (Unit Root Test)
It is suggested that when dealing with time series data, a number of econometric issues can influence the
estimation of parameters using OLS. Regressing a time series variable on another time series variable using
the Ordinary Least Squares (OLS) estimation can obtain a very high R 2, although there is no meaningful
relationship between the variables. This situation reflects the problem of spurious regression between
totally unrelated variables generated by a non-stationary process. Therefore, prior to testing Cointegration
and implementing the Granger Causality test, econometric methodology needs to examine the stationarity;
for each individual time series, most macro economic data are non stationary, i.e. they tend to exhibit a
deterministic and/or stochastic trend. Therefore, it is recommended that a stationarity (unit root) test be
carried out to test for the order of integration. A series is said to be stationary if the mean and variance are
time-invariant. A non-stationary time series will have a time dependent mean or make sure that the
variables are stationary, because if they are not, the standard assumptions for asymptotic analysis in the
Granger test will not be valid. Therefore, a stochastic process that is said to be stationary simply implies
that the mean [(E(Yt)] and the variance [Var(Yt)] of Y remain constant over time for all t, and the
covariance [covar(Yt, Ys)] and hence the correlation between any two values of Y taken from different time
periods depends on the difference apart in time between the two values for all t≠s. Since standard
regression analysis requires that data series be stationary, it is obviously important that we first test for this
requirement to determine whether the series used in the regression process is a difference stationary or a
trend stationary. The Augmented Dickey-Fuller (ADF) test is used. To test the stationary of variables, we
use the Augmented Dickey Fuller (ADF) test which is mostly used to test for unit root. Following equation
checks the stationarity of time series data used in the study:
n
Δy β +βt+αy γ ΣΔy ε
t= 1 1 t-1 + t-1 + t
t=1
Where ε is white nose error term in the model of unit root test, with a null hypothesis that variable has unit
t
root. The ADF regression test for the existence of unit root of yt that represents all variables (in the natural
logarithmic form) at time t. The test for a unit root is conducted on the coefficient of yt-1 in the regression.
If the coefficient is significantly different from zero (less than zero) then the hypothesis that y contains a
unit root is rejected. The null and alternative hypothesis for the existence of unit root in variable y t is H0; α
= 0 versus H1: α < 0. Rejection of the null hypothesis denotes stationarity in the series.
If the ADF test-statistic (t-statistic) is less (in the absolute value) than the Mackinnon critical t-values, the
null hypothesis of a unit root can not be rejected for the time series and hence, one can conclude that the
series is non-stationary at their levels. The unit root test tests for the existence of a unit root in two cases:
with intercept only and with intercept and trend to take into the account the impact of the trend on the
series.
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The PP tests are non-parametric unit root tests that are modified so that serial correlation does not affect
their asymptotic distribution. PP tests reveal that all variables are integrated of order one with and without
linear trends, and with or without intercept terms.
Phillips–Perron test (named after Peter C. B. Phillips and Pierre Perron) is a unit root test. That is, it is used
in time series analysis to test the null hypothesis that a time series is integrated of order 1. It builds on the
Dickey–Fuller test of the null hypothesis δ = 0 in Δ , here Δ is the first difference
operator. Like the augmented Dickey–Fuller test, the Phillips–Perron test addresses the issue that the process
generating data for yt might have a higher order of autocorrelation than is admitted in the test equation -
making yt − 1 endogenous and thus invalidating the Dickey–Fuller t-test. Whilst the augmented Dickey–Fuller
test addresses this issue by introducing lags of Δ yt as regressors in the test equation, the Phillips–Perron test
makes a non-parametric correction to the t-test statistic. The test is robust with respect to unspecified
autocorrelation and heteroscedasticity in the disturbance process of the test equation.
Once the number of unit roots in the series was decided, the next step before applying Johansen’s (1988)
co-integration test was to determine an appropriate number of lags to be used in estimation. Second,
Eagle-Granger residual based test tests the existence of co integration among the variables-FT and GDP at
constant prices for the economy. Third, if a co integration relationship does not exist, VAR analysis in the
first difference is applied, however, if the variables are co integrated, the analysis continues in a
cointegration framework.
Step-III: Testing for Cointegration Test(Johansen Approach)
Cointegration, an econometric property of time series variable, is a precondition for the existence of a long
run or equilibrium economic relationship between two or more variables having unit roots (i.e. Integrated
of order one). The Johansen approach can determine the number of co-integrated vectors for any given
number of non-stationary variables of the same order. Two or more random variables are said to be
cointegrated if each of the series are themselves non – stationary. This test may be regarded as a long run
equilibrium relationship among the variables. The purpose of the Cointegration tests is to determine
whether a group of non – stationary series is cointegrated or not.
Having concluded from the ADF results that each time series is non-stationary, i.e it is integrated of order
one I(1), we proceed to the second step, which requires that the two time series be co-integrated. In other
words, we have to examine whether or not there exists a long run relationship between variables (stable and
non-spurious co-integrated relationship) . In our case, the mission is to determine whether or not export(EX)
and economic growth(GDP) variables have a long-run relationship in a bivariate framework. Engle and
Granger (1987) introduced the concept of cointegration, where economic variables might reach a long-run
equilibrium that reflects a stable relationship among them. For the variables to be co-integrated, they must
be integrated of order one (non-stationary) and the linear combination of them is stationary I(0).
The crucial approach which is used in this study to test r cointegration is called the Johansen
cointegration approach. The Johanson approach can determine the number of cointegrated vectors for any
given number of non-stationary variables of the same order.
Step-IV: The Granger Causality test :
Causality is a kind of statistical feedback concept which is widely used in the building of forecasting
models. Historically, Granger (1969) and Sim (1972) were the ones who formalized the application of
causality in economics. Granger causality test is a technique for determining whether one time series is
significant in forecasting another (Granger. 1969). The standard Granger causality test (Granger, 1988)
seeks to determine whether past values of a variable helps to predict changes in another variable. The
definition states that in the conditional distribution, lagged values of Yt add no information to explanation
of movements of Xt beyond that provided by lagged values of Xt itself (Green, 2003). We should take note
of the fact that the Granger causality technique measures the information given by one variable in
explaining the latest value of another variable. In addition, it also says that variable Y is Granger caused by
variable X if variable X assists in predicting the value of variable Y. If this is the case, it means that the
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lagged values of variable X are statistically significant in explaining variable Y. The null hypothesis (H0)
that we test in this case is that the X variable does not Granger cause variable Y and variable Y does not
Granger cause variable X.In summary, one variable (Xt) is said to granger cause another variable (Yt) if the
lagged values of Xt can predict Yt and vice-versa.
EX and GDP are, in fact, interlinked and co-related through various channel. There is no theoretical or
empirical evidence that could conclusively indicate sequencing from either direction. For this reason, the
Granger Causality test was carried out on EX and GDP.
The spirit of Engle and Granger (1987) lies in the idea that if the two variables are integrated as order one,
I(1), and both residuals are I(0), this indicates that the two variables are cointegrated. The Granger theorem
states that if this is the case, the two variables could be generated by a dynamic relationship from GDP to
EX and, vise versa.
Therefore,a time series X is said to Granger-cause Y if it can be shown through a series of F-tests on lagged
values of X (and with lagged values of Y also known) that those X values predict statistically significant
information about future values of Y. In the context of this analysis, the Granger method involves the
estimation of the following equations:
If causality (or causation) runs from EX to GDP, we have:
dLnGDPit = ηi+ Σα11dLnGDPi,t-1+ Σβ11dLnEXi,t-1 +ε1t ……………………………………………(4)
If causality (or causation) runs from GDP to EX, it takes the form:
dLnEXit = ηi+Σα12dLn EXi,t-1 +Σβ12dLnGDPi,t-1 +λECMit+ε2t…………………………………(5)
where, GDP t and EXt represent gross domestic product and export respectively, ε it is uncorrelated
stationary random process, and subscript t denotes the time period. In equation 4,failing to reject: H 0: α11 =
β11 =0 implies that educational expenditure does not Granger cause economic growth. On the other
hand, in equation5,failing to reject H0: α12= β12 =0 implies that economic growth via GDP growth does not
Granger cause educational expenditure.
The decision rule:
From equation (4), dLnEXi t-1Granger causes dLnGDPit if the coefficient of the lagged values of EX as a
group (β11) is significantly different from zero based on F-test (i.e., statistically significant). Similarly, from
equation (5), dLnGDPi,t-1 Granger causes dLnEXit if β12is statistically significant.
Step V: Error Correcting Model (ECM) and Short Term Causality Test :
Error correction mechanism was first used by Sargan (1984), later adopted, modified and popularized by
Engle and Granger (1987). By definition, error correction mechanism is a means of reconciling the
short-run behaviour (or value) of an economic variable with its long-run behaviour (or value). An important
theorem in this regard is the Granger Representation Theorem which demonstrates that any set of
cointegrated time series has an error correction representation, which reflects the short-run adjustment
mechanism.
Co- integration relationships just reflect the long term balanced relations between relevant variables. In
order to cover the shortage, correcting mechanism of short term deviation from long term balance could be
cited. At the same time, as the limited number of years, the above test result may cause disputes
(Christpoulos and Tsionas, 2004). Therefore, under the circumstance of long term causalities, short term
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causalities should be further tested as well. Empirical works based on time series data assume that the
underlying time series is stationary. However, many studies have shown that majority of time series
variables are nonstationary or integrated of order 1 (Engle and Granger, 1987). The time series properties of
the data at hand are therefore studied in the outset. Formal tests will be carried out to find the time series
properties of the variables. If the variables are I (1), Engle and Granger (1987) assert that causality must
exist in, at least, one direction. The Granger causality test is then augmented with an error correction term
(ECT) and the error correcting models could be built as below:
dLnGDPit = ηi+ Σα11dLnGDPi,t-1+ Σβ11dLnEXi,t-1+ λECMit+εit………………….…………………(6)
dLnEXit = ηi+Σα12dLn EXi,t-1 +Σβ12dLnGDPi,t-1 +λECMi t+εit…………………………….(7)
Where t represents year, d rerepresents first order difference calculation, ECMit represents the errors of
long term balance which is obtained from the long run co-integrating relationship between economic
growth and educational expenditure. If λ = 0 is rejected, error correcting mechanism happens, and the
tested long term causality is reliable, otherwise, it could be unreliable. If β1=0 is rejected, and then the
short term causality is proved, otherwise the short term causality doesn’t exist.
4. Analysis of the Result:
4.1.Ordinary Least Square Technique:
This section presents the nexus between export and economic growth in terms of OLS Technique.
[Insert Table-1 here]
In ordinary least square Method, we reject the hypothesis that there is no relationship between the variable
and the results of the Ordinary Least Squares Regression are summarized in the Table 1. The empirical
analysis on basis of ordinary Least Square Method suggests that there is positive relationship between
export and GDP and vice versa.
4.2.Unit Root Test:
Table 2&3 present the results of the unit root test. The results show that both variables of our interest,
namely LnGDP and Ln EX attained stationarity after first differencing, I(1), using PP test. The
augmented Dickey Fuller Test fails to provide result of stationary at first difference at all lag differences.
[Insert Table-2 here]
Table (2) presents the results of the unit root test for the two variables for their levels. The results
indicate that the null hypothesis of a unit root can not be rejected for the given variable and, hence, one can
conclude that the variables are not stationary at their levels.
To determine the stationarity property of the variable, the same test above was applied to the first
differences. Results from table (3) revealed that the ADF value is not greater than the critical t-value at
1% ,5% and 10%level of significance for all variables. Based on these results, the null hypothesis that the
series have unit roots in their differences can not be rejected.
An inspection of the figures reveals in table-5 that each series is first difference stationary at 1%,5% and
10% level using the PP test. However, the ADF test result is not as impressive, as all the variables did not
pass the differenced stationarity test at the one, five and ten percent levels. We therefore rely on the PP test
result as a basis for a cointegration test among all stationary series of the same order meaning that the two
series are stationary at their first differences [they are integrated of the order one i.e I(1)].
[Insert Table-3 here]
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[Insert Table-4 here]
[Insert Table-5 here]
4.3.Cointegration Test:
Having established the time series properties of the data, the test for presence of long-run relationship
between the variables using the Johansen and Juselius (1992) LR statistic for cointegration was conducted.
The crucial approach which is used in this study to test cointegration is called the Johansen cointegration
approach. The Johanson approach can determine the number of cointegrated vectors for any given number
of non-stationary variables of the same order. The results reported in table (6) suggest that the null
hypothesis of no cointegrating vectors can be rejected at the 1% level of significance. It can be seen from
the Likelihood Ratio (L.R.) that we have a single co-integration equations. In other words, there exists one
linear combination of the variables.
[Insert Table-6 here]
The normalized cointegrating equation is
LnGDP = -9.825+ 0.9791 LnEX -----------------------(7)
((17.50)
The standard error is in the parentheses the behavioural parameter (EX) is statistically significant at 1%.
Estimating the long-run relationship, the results are contained in equation (7) which shows positive
relationship between education and economic growth. Precisely, 1% increase in export raises the level of
GDP by 97.91%.Therefore,the normalized cointegration equation reveals that there is a positive
relationship between export(EX) and GDP(Economic growth).Looking at the results, the normalized
cointegrating equation (7) reveals that in the long-run, export affects economic growth positively in India.
Interestingly, this result is impressive because 1% change in export volume leads to about 98 percent
change in economic growth via GDP growth in the same direction, over the long-run horizon. This of
course is highly significant judging from the t-statistic.
4.4.Granger Causality Test :
The results of Pairwise Granger Causality between economic growth (GDP) and export (EX) are contained
in Table 7. The results reveal the existence of a bi-directional causality which runs from economic growth
(GDP) to investment in education (EX) and vice versa.
[Insert Table-7 here]
The null hypotheses of the Granger-Causality test are:
H0: X ≠ Y (X does not granger-cause Y)
H1: X ≠Y (X does Granger-cause Y)
We have found that both for the Ho of “LNEX does not Granger Cause LNGDP” and Ho of “LNGDP
does not Granger Cause LNEX” , we cannot reject the Ho since the F-statistics are rather small and most
of the probability values are close to or even greater than 0.1 at the lag length of 1 to 4. Therefore, we
accept the Ho and conclude that LNEX does not Granger Cause LNGDP and LNGDP does not Granger
Cause LNEX.
The above results generally show that causality is bidirectional and therefore we find that the direction of
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causality between export indicators and economic growth in India is generally bidirectional (causality runs
in both directions).
4.5.Error Correction Mechanism(VECM):
The result (Table 8) indicates that the ECM in model-1 tested by equation (6) is positive and passes the
significance test by 0.05, which means error correction happens, and the pulling function of export on
GDP is proved. The ECM in model-2 tested by equation (7) is positive and passes the test, which means
that there exists mutual causality between export(EX) and GDP. According to the co-integration equations,
we can see they are positively related. That is to say, exports have positively pulling function on GDP; on
the other hand, the GDP growth will also promote the export volume of the country. So it can be concluded
that exports and GDP are mutually causal.
[Insert Table-8 here]
5. Conclusion:
The paper tries to assess empirically, the relationship between export and economic growth in India using
annual data over the period 1972-73 to 2010-11. The unit root properties of the data were examined using the
Augmented Dickey Fuller test(ADF) after which the cointegration and causality tests were conducted. The
error correction models were also estimated in order to examine the short –run dynamics. The major findings
include the following:
The unit root test clarified that both economic growth and export are non-stationary at the level data but
found stationary at the first differences.Therefore,the series of both variables of our consideration-EX and
GDP, namely, export and economic growth were found to be integrated of order one using the
Phillips-Perron tests for unit root.
The cointegration test confirmed that economic growth and export are cointegrated, indicating an
existence of long run equilibrium relationship between the two as confirmed by the Johansen cointegration
test results.
The Granger causality test finally confirmed the presence of bi-directional causality which runs from
economic growth to export and vice-versa.
export and GDP are mutually causal.
The results recommend that an export-led growth strategy through contributing various incentives
should be adopted and continued to have greater access over international market and a long-term
relationship between exports and economic growth via GDP growth has been emerged. Export promotion
has a positive influence on growth in GDP. The results confirm further the advantages of an export-led
growth strategy for India. India can expand its market by exporting products to the international markets.
Policies focusing on export promotion should be used effectively to fabricate export capacity in order to
enhance economic growth.
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Countries”, International Advances in Economic Research, Vol. 7.
Table: 1:Result of OLS Technique
Variable Dependent variable is LnGDP
Coefficient SE t R2 F Statistic
ratio
Ln EX 0.359008 0.010424 34.44 0.97 1186.20
Dependent variable is LnEX
LnGDP 2.703410 0.078493 34.44 0.97 1186.20
Ho: There is no relationship between the variables; H1: There is relationship between the variables
Table 2: Unit Root Test: The Results of the Augmented Dickey Fuller (ADF) Test for Levels with an
Intercept and Linear Trend
Intercept only Intercept&Trend
Variable ADF(0) ADF(1) ADF(2) ADF(0) ADF(1) ADF(2)
LnGDP 2.214 2.097 2.108 -0.7376 -0.4758 -0.7265
AIC -3.028 -2.964 -2.906 -3.013 -2.933 -2.894
SBC -2.941 -2.831 -2.728 -2.883 -2.757 -2.672
1% Critical Value* -3.62 1% Critical Value* -4.22
5% Critical Value -2.94 5% Critical Value -3.53
10% Critical Value -2.61 10% Critical Value -3.20
Ln EX 0.381 0.6001 0.7499 -1.479 -4.134 -2.906
AIC -1.860 -1.802 -1.917 -1.872 -10.129 -2.117
SBC -1.774 -1.672 -1.741 -1.743 -9.954 -1.896
1% Critical Value* -3.62 1% Critical Value* -4.23
5% Critical Value -2.94 5% Critical Value -3.53
10% Critical Value -2.61 10% Critical Value -3.20
Ho: series has unit root; H1: series is trend stationary
*MacKinnon critical values for rejection of hypothesis of a unit root.
AIC stands for Akaike info criterion
SBC stands for Schwarz Bayesian criterion
Table 3: Unit Root Test: The Results of the Augmented Dickey Fuller (ADF) Test for the First
Differences with an Intercept and Linear Trend
Intercept only Intercept&Trend
Variable ADF(0) ADF(1) ADF(2) ADF(0) ADF(1) ADF(2)
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LnGDP -5.677 -3.429 -2.517 -6.411 -4.333 -3.240
AIC -2.894 -2.830 -2.782 -2.981 -2.934 -2.841
SBC -2.806 -2.696 -2.602 -2.849 -2.756 -2.616
1% Critical Value* -3.62 1% Critical Value* -4.23
5% Critical Value -2.94 5% Critical Value -3.54
10% Critical Value -2.61 10% Critical Value -3.20
LnEX -5.512 -2.884 -2.824 -5.491 -2.980 -2.973
AIC -1.846 -1.955 -1.884 -1.809 -1.931 -1.863
SBC -1.759 -1.823 -1.706 -1.678 -1.755 -1.641
1% Critical Value* -3.62 1% Critical Value* -4.23
5% Critical Value -2.94 5% Critical Value -3.54
10% Critical Value -2.61 10% Critical Value -3.20
Ho: series has unit root; H1: series is trend stationary.
*MacKinnon critical values for rejection of hypothesis of a unit root.
AIC stands for Akaike info criterion
SBC stands for Schwarz Bayesian criterion
Table 4: Unit Root Test: The Results of the Phillips-Perron Test for First Differences with an
Intercept and Linear Trend
Intercept only Intercept&Trend
Variable ADF(0) ADF(1) ADF(2) ADF(0) ADF(1) ADF(2)
LnGDP 2.214 2.389 2.490 -0.7376 -0.6763 -0.6617
AIC -3.028 -3.028 -3.028 -3.013 -3.013 -3.013
SBC -2.941 -2.941 -2.941 -2.883 -2.883 -2.883
1% Critical Value* -3.62 1% Critical Value* -4.22
5% Critical Value -2.94 5% Critical Value -3.53
10% Critical Value -2.61 10% Critical Value -3.20
Ln EX 0.3808 0.3680 0.3019 -1.479 -1.548 -1.737
AIC -1.860 -1.860 -1.860 -1.872 -1.872 -1.872
SBC -1.774 -1.774 -1.774 -1.743 -1.743 -1.742
1% Critical Value* -3.62 1% Critical Value* -4.23
5% Critical Value -2.94 5% Critical Value -3.53
10% Critical Value -2.61 10% Critical Value -3.20
Ho: series has unit root; H1: series is trend stationary
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*MacKinnon critical values for rejection of hypothesis of a unit root.
AIC stands for Akaike info criterion
SBC stands for Schwarz Bayesian criterion
Table 5: Unit Root Test: The Results of the Phillips-Perron Test for First Differences with an
Intercept and Linear Trend
Intercept only Intercept&Trend
Variable ADF(0) ADF(1) ADF(2) ADF(0) ADF(1) ADF(2)
LnGDP -5.676 -5.678 -5.690 -6.411 -6.409 -6.415
AIC -2.894 -2.894 -2.894 -2.981 -2.981 -2.981
SBC -2.806 -2.806 -2.806 -2.849 -2.849 -2.849
1% Critical Value* -3.62 1% Critical Value* -4.23
5% Critical Value -2.94 5% Critical Value -3.5
10% Critical Value -2.61 10% Critical Value -3.20
Ln EX -5.512 -5.498 -5.585 -5.491 -5.473 -5.545
AIC -1.846 -1.846 -1.846 -1.809 -1.809 -1.809
SBC -1.759 -1.759 -1.759 -1.678 -1.678 -1.678
1% Critical Value* -3.62 1% Critical Value* -4.22
5% Critical Value -2.94 5% Critical Value -3.53
10% Critical Value -2.6 10% Critical Value -3.20
Ho: series has unit root; H1: series is trend stationary
*MacKinnon critical values for rejection of hypothesis of a unit root.
AIC stands for Akaike info criterion
SBC stands for Schwarz Bayesian criterion
Table 6. Johansen Cointegration Tests:
Hypothesized Eigen value Likelihood Ratio 5% critical 1% critical
N0. Of CE (s) value value
None ** 0.391276 21.18541 19.96 24.60
At most 1 0.087980 3.315365 9.24 12.97
Ho: has no co-integration; H1: has co-integration
*(**) denotes rejection of the hypothesis at 5%(1%) significance level
L.R. test indicates one cointegrating equation(s) at 5% significance level
Table: 7: Granger Casuality test
Null Hypothesis Lag Observations. F-statistics Probability Decision
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LNEX does not 1 37* 1.37426 0.24923 Accept
Granger Cause 2 36 0.93287 0.40419 Accept
LNGDP
3 35 0.50957 0.67892 Accept
4 34 0.53263 0.71292 Accept
LNGDP does not 1 37 0.73917 0.39595 Accept
Granger Cause 2 36 0.60438 0.55273 Accept
LNEX
3 35 0.93755 0.43567 Accept
4 34 0.53712 0.70977 Accept
*Observations. after lag.
Table:8: Short term causality test for time series data(VECM)
Variables Model-1 Model-2
D(LNGDP) D(LNEX)
ECM -0.004384* -0.006725
(0.00168) (0.00277)
(-2.61752) (-2.43097)
D(LNGDP(-1)) -0.093354 0.168664
(0.18076) (0.29854)
(-0.51646) (0.56495)
D(LNGDP(-2)) -0.042965 -0.412777
(0.18030) (0.29779)
(-0.23829) (-1.38612)
D(LNEX(-1)) 0.071269 0.015474
(0.10234) (0.16903)
(0.69637) (0.09154)
D(LNEX(-2)) -0.039312 0.405279
(0.10419) (0.17208)
(-0.37731) (2.35511)
R-squared 0.168063 0.196713
F-statistic 3.236361 3.390904
*indicates panel data pass the significance test by 95% level.
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