Structural breaks in data can lead to multiple regression relationships that need to be identified and controlled for. A first step is to plot the variables and look for any displacements over time which could indicate a structural break. Adding a single dummy variable can help account for an identified break based on the historical context, but having multiple dummies makes the results difficult to interpret. Dummies should only be used to represent breaks that can be reasonably explained by the background of the time series.
This document provides an overview of cost functions, including:
- How cost functions are derived from cost minimization problems
- Properties of cost functions such as non-negativity and homogeneity
- How conditional input demand functions can be derived from cost functions using Shephard's Lemma
- Concepts of short-run and long-run cost functions, and marginal and average costs
- Economies of scale and scope as measured by cost functions
- Revenue and profit functions, and how they are related to cost functions
This document provides an overview of econometrics. It defines econometrics as the quantitative analysis of economic phenomena based on concurrent theory and observation, using appropriate statistical methods. Econometrics gives empirical content to economic theory by providing numerical estimates of relationships hypothesized by theory, like the inverse relationship between price and quantity demanded. The document outlines the methodology of econometrics, including specifying mathematical and statistical models, collecting data, estimating parameters, hypothesis testing, and using models for forecasting or policy purposes. It provides an example estimating the price-demand relationship for rice to illustrate the econometrics methodology.
This Presentation is tailor made for those who are willing to get an overview of Econometrics as to what it means, how it works and the methodology it follows.
This document provides an overview of an analytical methods course for economics and finance. It introduces the course staff and coordinators. It describes how econometrics can be used to answer quantitative questions about economics and business. It also discusses different types of economic data and some basic mathematical and statistical concepts needed for the course, including summation, probability, and random variables. An important note reminds students about class attendance, staff consultation hours, accessing learning materials, and preparing for an upcoming online quiz.
This document discusses multicollinearity in econometrics. Multicollinearity occurs when there is a near-perfect linear relationship among independent variables. It can lead to unstable parameter estimates and high standard errors. Symptoms include high standard errors, unexpected parameter signs or magnitudes, and jointly significant but individually insignificant variables. Diagnosis involves examining variable correlations and testing joint significance. The variance inflation factor (VIF) measures the impact of multicollinearity, with values above 2 indicating a potential problem. Remedies include acquiring more data, dropping problematic variables, or reformulating the model, though these can introduce new issues. Multicollinearity alone does not invalidate estimates.
1. The document discusses the nature of regression analysis, which involves studying the dependence of a dependent variable on one or more explanatory variables, with the goal of estimating or predicting the average value of the dependent variable based on the explanatory variables.
2. It provides examples of regression analysis, such as studying how crop yield depends on factors like temperature, rainfall, and fertilizer. It also distinguishes between statistical and deterministic relationships, and notes that regression analysis indicates dependence but does not necessarily imply causation.
3. Regression analysis differs from correlation analysis in that it treats the dependent and explanatory variables asymmetrically, with the goal of prediction rather than just measuring the strength of the linear association between variables.
This document discusses econometrics and its applications. It defines econometrics as using statistical methods to estimate economic relationships and test economic theories. Econometrics allows estimating relationships between economic variables, testing hypotheses, and forecasting. It helps explain qualitative economic data quantitatively and evaluate government policies. Common econometric methods discussed include simple and multiple linear regression, estimation theory, and time series analysis. The document also notes some limitations of econometrics, such as not proving causation and possible issues with data interpretation.
Structural breaks in data can lead to multiple regression relationships that need to be identified and controlled for. A first step is to plot the variables and look for any displacements over time which could indicate a structural break. Adding a single dummy variable can help account for an identified break based on the historical context, but having multiple dummies makes the results difficult to interpret. Dummies should only be used to represent breaks that can be reasonably explained by the background of the time series.
This document provides an overview of cost functions, including:
- How cost functions are derived from cost minimization problems
- Properties of cost functions such as non-negativity and homogeneity
- How conditional input demand functions can be derived from cost functions using Shephard's Lemma
- Concepts of short-run and long-run cost functions, and marginal and average costs
- Economies of scale and scope as measured by cost functions
- Revenue and profit functions, and how they are related to cost functions
This document provides an overview of econometrics. It defines econometrics as the quantitative analysis of economic phenomena based on concurrent theory and observation, using appropriate statistical methods. Econometrics gives empirical content to economic theory by providing numerical estimates of relationships hypothesized by theory, like the inverse relationship between price and quantity demanded. The document outlines the methodology of econometrics, including specifying mathematical and statistical models, collecting data, estimating parameters, hypothesis testing, and using models for forecasting or policy purposes. It provides an example estimating the price-demand relationship for rice to illustrate the econometrics methodology.
This Presentation is tailor made for those who are willing to get an overview of Econometrics as to what it means, how it works and the methodology it follows.
This document provides an overview of an analytical methods course for economics and finance. It introduces the course staff and coordinators. It describes how econometrics can be used to answer quantitative questions about economics and business. It also discusses different types of economic data and some basic mathematical and statistical concepts needed for the course, including summation, probability, and random variables. An important note reminds students about class attendance, staff consultation hours, accessing learning materials, and preparing for an upcoming online quiz.
This document discusses multicollinearity in econometrics. Multicollinearity occurs when there is a near-perfect linear relationship among independent variables. It can lead to unstable parameter estimates and high standard errors. Symptoms include high standard errors, unexpected parameter signs or magnitudes, and jointly significant but individually insignificant variables. Diagnosis involves examining variable correlations and testing joint significance. The variance inflation factor (VIF) measures the impact of multicollinearity, with values above 2 indicating a potential problem. Remedies include acquiring more data, dropping problematic variables, or reformulating the model, though these can introduce new issues. Multicollinearity alone does not invalidate estimates.
1. The document discusses the nature of regression analysis, which involves studying the dependence of a dependent variable on one or more explanatory variables, with the goal of estimating or predicting the average value of the dependent variable based on the explanatory variables.
2. It provides examples of regression analysis, such as studying how crop yield depends on factors like temperature, rainfall, and fertilizer. It also distinguishes between statistical and deterministic relationships, and notes that regression analysis indicates dependence but does not necessarily imply causation.
3. Regression analysis differs from correlation analysis in that it treats the dependent and explanatory variables asymmetrically, with the goal of prediction rather than just measuring the strength of the linear association between variables.
This document discusses econometrics and its applications. It defines econometrics as using statistical methods to estimate economic relationships and test economic theories. Econometrics allows estimating relationships between economic variables, testing hypotheses, and forecasting. It helps explain qualitative economic data quantitatively and evaluate government policies. Common econometric methods discussed include simple and multiple linear regression, estimation theory, and time series analysis. The document also notes some limitations of econometrics, such as not proving causation and possible issues with data interpretation.
I. The document discusses the method of ordinary least squares (OLS) regression analysis. OLS chooses estimates that minimize the sum of squared residuals between the actual and predicted y-values.
II. OLS provides point estimates for regression parameters and makes assumptions such as a linear relationship between variables, independent and homoscedastic errors, and no autocorrelation.
III. Monte Carlo experiments can test the statistical properties of OLS by repeatedly simulating the regression of randomly generated data on fixed x-values and checking if the average estimates equal the true parameter values.
Econometrics combines economic theory, mathematics, statistics, and economic data to empirically test economic relationships and quantify economic models. It involves stating an economic theory, specifying the mathematical and econometric models, obtaining data, estimating model parameters, testing hypotheses, forecasting, and using models for policy purposes. The econometrician adds a stochastic error term to account for uncertainty from omitted variables, data limitations, intrinsic randomness, and incorrect model specification. Econometrics aims to numerically measure relationships posited by economic theories.
Advanced Econometrics by Sajid Ali Khan Rawalakot: 0334-5439066Sajid Ali Khan
This document appears to be the introduction or table of contents to a textbook on advanced econometrics. It includes 10 chapters that cover topics such as simple linear regression, multiple linear regression, dummy variables, autocorrelation, and simultaneous equation systems. The introduction defines econometrics and discusses its goals of policy making, forecasting, and analyzing economic theories using quantitative methods. It also outlines the methodology of econometrics, which involves stating an economic theory, specifying mathematical and statistical models, collecting data, estimating parameters, testing hypotheses, forecasting, and using models for control or policy purposes.
This document discusses the methodology of econometrics. It begins by defining econometrics as applying economic theory, mathematics and statistical inference to analyze economic phenomena. It then outlines the typical steps in an econometric analysis: 1) stating an economic theory or hypothesis, 2) specifying a mathematical model, 3) specifying an econometric model, 4) collecting data, 5) estimating parameters, 6) hypothesis testing, 7) forecasting, and 8) using the model for policy purposes. As an example, it walks through Keynes' consumption theory using U.S. consumption and GDP data to estimate the marginal propensity to consume.
This document discusses autocorrelation in time series data and its effects on regression analysis. It defines autocorrelation as errors in one time period carrying over into future periods. Autocorrelation can be caused by factors like inertia in economic cycles, specification bias, lags, and nonstationarity. While OLS estimators remain unbiased with autocorrelation, they become inefficient and hypothesis tests are invalid. Autocorrelation can be detected using graphical analysis or formal tests like the Durbin-Watson test and Breusch-Godfrey test. The Cochrane-Orcutt procedure is also described as a way to transform data and remove autocorrelation.
The document compares the monetary and Keynesian approaches to economic stability. The monetary (or monetarist) approach is based on the role of money in stabilizing aggregate demand, and believes that limiting government intervention and controlling the money supply are key. The Keynesian approach focuses on the role of government spending in stabilizing aggregate demand, and does not restrict government intervention. It believes fiscal policy tools like tax rates and government spending are most important for achieving economic stability, especially during downturns when suggested solutions include increasing various types of spending.
This document discusses multicollinearity in regression analysis. It defines multicollinearity as an exact or near-exact linear relationship between explanatory variables. In cases of perfect multicollinearity, individual regression coefficients cannot be estimated. Near or imperfect multicollinearity is more common in real data and can lead to less precise coefficient estimates with wider confidence intervals. The document discusses various methods for detecting multicollinearity, such as auxiliary regressions and variance inflation factors, and potential remedies like dropping or transforming variables. However, multicollinearity diagnosis depends on the specific data sample and goals of the analysis.
Covariance and correlation(Dereje JIMA)Dereje Jima
The document discusses covariance and correlation, which are mathematical models used to assess relationships between variables. Covariance measures how two variables change together, while correlation measures both the strength and direction of the linear relationship between variables. Correlation coefficients range from -1 to 1, where values closer to 1 or -1 indicate a strong linear relationship and values closer to 0 indicate no linear relationship. The document also discusses partial correlation and multiple correlation, which measure relationships while controlling for additional variables. Factors that can affect correlation analyses include sample size and outliers.
1. Multinomial logistic regression allows modeling of nominal outcome variables with more than two categories by calculating multiple logistic regression equations to compare each category's probability to a reference category.
2. The document provides an example of using multinomial logistic regression to model student program choice (academic, general, vocational) based on writing score and socioeconomic status.
3. The model results show that writing score significantly impacts the choice between academic and general/vocational programs, while socioeconomic status also influences general versus academic program choice.
This document discusses panel data and methods for analyzing it. Panel data contains observations on multiple entities like individuals, states, or school districts that are observed at different points in time. This allows controlling for factors that are constant over time but vary across entities. Fixed effects regression is introduced as a method that eliminates the effect of any time-invariant characteristics. The document provides examples of how to specify fixed effects models using binary regressors or demeaning the data, and notes these produce identical estimates.
This document discusses concepts and techniques for time series analysis. It defines a time series as any series of data that varies over time, and provides examples like GDP and stock prices. It outlines precautions for using time series data in econometric models, such as checking for stationarity and guarding against spurious regressions. Stationary and non-stationary time series are defined, and unit root tests like the Dickey-Fuller test are introduced. The concepts of cointegration and error correction models are also covered, along with the Granger causality test.
The document provides information about several theoretical probability distributions including the normal, t, and chi-square distributions. It discusses key properties such as the mean, standard deviation, and shape of the normal distribution curve. Examples are given to demonstrate how to calculate areas under the normal distribution curve and find z-scores. The t-distribution is introduced as similar to the normal but used for smaller sample sizes. The chi-square distribution is defined as used for hypothesis testing involving categorical data.
The cob web model analyzes price and output dynamics in markets where supply responds to price with a time lag. It assumes that producers base current supply on previous period's price. If demand changes but supply cannot instantly adjust, prices and quantities will oscillate over time as they converge towards equilibrium. The model can produce convergent cycles that stabilize at equilibrium or divergent cycles where prices and outputs fluctuate further from equilibrium with each cycle. It is used to study agricultural commodity markets where production adjustments face time lags.
Econometrics involves applying statistical tools to economic data to analyze economic phenomena numerically. It uses economic theory, mathematics, and statistics. The methodology of econometrics includes: 1) Stating an economic theory or hypothesis, 2) Specifying a mathematical model of the theory, 3) Specifying an econometric model, 4) Obtaining data, 5) Estimating the parameters of the econometric model using regression analysis, and 6) Testing hypotheses and using the model for forecasting, prediction, control or policy purposes.
This document provides instructions for working with time series data in Stata. It discusses how to properly format date variables from various formats into a date format recognized by Stata. It also explains how to declare time series data and use time series operators to generate lags, leads, differences, and seasonal values. Statistical commands for exploring autocorrelation and the relationship between two time series are also covered.
This document discusses heteroscedasticity, which occurs when the error variance is not constant. It provides examples of when the variance of errors may change, such as with income level or outliers. Graphical methods are presented for detecting heteroscedasticity by examining patterns in residual plots. Formal tests are also described, including the Park test which regresses the log of the squared residuals on explanatory variables, and the Glejser test which regresses the absolute value of residuals on variables related to the error variance. Detection of heteroscedasticity is important as it violates assumptions of the classical linear regression model.
The presentation aims to explain the meaning of ECONOMETRICS and why this subject is studied as a separate discipline.
The reference is based on the book "BASIC ECONOMETRICS" by Damodar N. Gujarati.
For further explanation, check out the youtube link:
https://youtu.be/S3SUDiVpUGU
Econometrics is the application of statistical and mathematical methods to economic data in order to test economic theories and estimate relationships between economic variables. The methodology of econometrics involves stating an economic theory or hypothesis, specifying the theory mathematically and as an econometric model, obtaining data, estimating the model, testing hypotheses, making forecasts, and using the model for policy purposes. Regression analysis is a key tool in econometrics that relates a dependent variable to one or more independent variables, with an error term included to account for the inexact nature of economic relationships.
This document discusses the use of dummy variables in econometric modeling. It begins by explaining that some variables cannot be quantified numerically and provides examples where dummy variables would be used. It then discusses how dummy variables are incorporated into regression models, including intercept dummy variables, slope dummy variables, and dummy variables for multiple categories. The document also covers seasonal dummy variables and concludes by explaining the Chow test and dummy variable test for testing structural stability using dummy variables.
Unit Root Test
To explain the concept of Unit root test
To highlight the different names of unit root test
Explaining the, Dickey Fuller unit root test Augmented Dickey Fuller test Phillips -Perron test
Unit Root Test
1: What is unit root?
2: How to check unit root?
3: Types of unit root test
4: Dickey fuller
5: Augmented dickey fuller
6: Phillip perron
7: Testing Unit Root on E-views
I. The document discusses the method of ordinary least squares (OLS) regression analysis. OLS chooses estimates that minimize the sum of squared residuals between the actual and predicted y-values.
II. OLS provides point estimates for regression parameters and makes assumptions such as a linear relationship between variables, independent and homoscedastic errors, and no autocorrelation.
III. Monte Carlo experiments can test the statistical properties of OLS by repeatedly simulating the regression of randomly generated data on fixed x-values and checking if the average estimates equal the true parameter values.
Econometrics combines economic theory, mathematics, statistics, and economic data to empirically test economic relationships and quantify economic models. It involves stating an economic theory, specifying the mathematical and econometric models, obtaining data, estimating model parameters, testing hypotheses, forecasting, and using models for policy purposes. The econometrician adds a stochastic error term to account for uncertainty from omitted variables, data limitations, intrinsic randomness, and incorrect model specification. Econometrics aims to numerically measure relationships posited by economic theories.
Advanced Econometrics by Sajid Ali Khan Rawalakot: 0334-5439066Sajid Ali Khan
This document appears to be the introduction or table of contents to a textbook on advanced econometrics. It includes 10 chapters that cover topics such as simple linear regression, multiple linear regression, dummy variables, autocorrelation, and simultaneous equation systems. The introduction defines econometrics and discusses its goals of policy making, forecasting, and analyzing economic theories using quantitative methods. It also outlines the methodology of econometrics, which involves stating an economic theory, specifying mathematical and statistical models, collecting data, estimating parameters, testing hypotheses, forecasting, and using models for control or policy purposes.
This document discusses the methodology of econometrics. It begins by defining econometrics as applying economic theory, mathematics and statistical inference to analyze economic phenomena. It then outlines the typical steps in an econometric analysis: 1) stating an economic theory or hypothesis, 2) specifying a mathematical model, 3) specifying an econometric model, 4) collecting data, 5) estimating parameters, 6) hypothesis testing, 7) forecasting, and 8) using the model for policy purposes. As an example, it walks through Keynes' consumption theory using U.S. consumption and GDP data to estimate the marginal propensity to consume.
This document discusses autocorrelation in time series data and its effects on regression analysis. It defines autocorrelation as errors in one time period carrying over into future periods. Autocorrelation can be caused by factors like inertia in economic cycles, specification bias, lags, and nonstationarity. While OLS estimators remain unbiased with autocorrelation, they become inefficient and hypothesis tests are invalid. Autocorrelation can be detected using graphical analysis or formal tests like the Durbin-Watson test and Breusch-Godfrey test. The Cochrane-Orcutt procedure is also described as a way to transform data and remove autocorrelation.
The document compares the monetary and Keynesian approaches to economic stability. The monetary (or monetarist) approach is based on the role of money in stabilizing aggregate demand, and believes that limiting government intervention and controlling the money supply are key. The Keynesian approach focuses on the role of government spending in stabilizing aggregate demand, and does not restrict government intervention. It believes fiscal policy tools like tax rates and government spending are most important for achieving economic stability, especially during downturns when suggested solutions include increasing various types of spending.
This document discusses multicollinearity in regression analysis. It defines multicollinearity as an exact or near-exact linear relationship between explanatory variables. In cases of perfect multicollinearity, individual regression coefficients cannot be estimated. Near or imperfect multicollinearity is more common in real data and can lead to less precise coefficient estimates with wider confidence intervals. The document discusses various methods for detecting multicollinearity, such as auxiliary regressions and variance inflation factors, and potential remedies like dropping or transforming variables. However, multicollinearity diagnosis depends on the specific data sample and goals of the analysis.
Covariance and correlation(Dereje JIMA)Dereje Jima
The document discusses covariance and correlation, which are mathematical models used to assess relationships between variables. Covariance measures how two variables change together, while correlation measures both the strength and direction of the linear relationship between variables. Correlation coefficients range from -1 to 1, where values closer to 1 or -1 indicate a strong linear relationship and values closer to 0 indicate no linear relationship. The document also discusses partial correlation and multiple correlation, which measure relationships while controlling for additional variables. Factors that can affect correlation analyses include sample size and outliers.
1. Multinomial logistic regression allows modeling of nominal outcome variables with more than two categories by calculating multiple logistic regression equations to compare each category's probability to a reference category.
2. The document provides an example of using multinomial logistic regression to model student program choice (academic, general, vocational) based on writing score and socioeconomic status.
3. The model results show that writing score significantly impacts the choice between academic and general/vocational programs, while socioeconomic status also influences general versus academic program choice.
This document discusses panel data and methods for analyzing it. Panel data contains observations on multiple entities like individuals, states, or school districts that are observed at different points in time. This allows controlling for factors that are constant over time but vary across entities. Fixed effects regression is introduced as a method that eliminates the effect of any time-invariant characteristics. The document provides examples of how to specify fixed effects models using binary regressors or demeaning the data, and notes these produce identical estimates.
This document discusses concepts and techniques for time series analysis. It defines a time series as any series of data that varies over time, and provides examples like GDP and stock prices. It outlines precautions for using time series data in econometric models, such as checking for stationarity and guarding against spurious regressions. Stationary and non-stationary time series are defined, and unit root tests like the Dickey-Fuller test are introduced. The concepts of cointegration and error correction models are also covered, along with the Granger causality test.
The document provides information about several theoretical probability distributions including the normal, t, and chi-square distributions. It discusses key properties such as the mean, standard deviation, and shape of the normal distribution curve. Examples are given to demonstrate how to calculate areas under the normal distribution curve and find z-scores. The t-distribution is introduced as similar to the normal but used for smaller sample sizes. The chi-square distribution is defined as used for hypothesis testing involving categorical data.
The cob web model analyzes price and output dynamics in markets where supply responds to price with a time lag. It assumes that producers base current supply on previous period's price. If demand changes but supply cannot instantly adjust, prices and quantities will oscillate over time as they converge towards equilibrium. The model can produce convergent cycles that stabilize at equilibrium or divergent cycles where prices and outputs fluctuate further from equilibrium with each cycle. It is used to study agricultural commodity markets where production adjustments face time lags.
Econometrics involves applying statistical tools to economic data to analyze economic phenomena numerically. It uses economic theory, mathematics, and statistics. The methodology of econometrics includes: 1) Stating an economic theory or hypothesis, 2) Specifying a mathematical model of the theory, 3) Specifying an econometric model, 4) Obtaining data, 5) Estimating the parameters of the econometric model using regression analysis, and 6) Testing hypotheses and using the model for forecasting, prediction, control or policy purposes.
This document provides instructions for working with time series data in Stata. It discusses how to properly format date variables from various formats into a date format recognized by Stata. It also explains how to declare time series data and use time series operators to generate lags, leads, differences, and seasonal values. Statistical commands for exploring autocorrelation and the relationship between two time series are also covered.
This document discusses heteroscedasticity, which occurs when the error variance is not constant. It provides examples of when the variance of errors may change, such as with income level or outliers. Graphical methods are presented for detecting heteroscedasticity by examining patterns in residual plots. Formal tests are also described, including the Park test which regresses the log of the squared residuals on explanatory variables, and the Glejser test which regresses the absolute value of residuals on variables related to the error variance. Detection of heteroscedasticity is important as it violates assumptions of the classical linear regression model.
The presentation aims to explain the meaning of ECONOMETRICS and why this subject is studied as a separate discipline.
The reference is based on the book "BASIC ECONOMETRICS" by Damodar N. Gujarati.
For further explanation, check out the youtube link:
https://youtu.be/S3SUDiVpUGU
Econometrics is the application of statistical and mathematical methods to economic data in order to test economic theories and estimate relationships between economic variables. The methodology of econometrics involves stating an economic theory or hypothesis, specifying the theory mathematically and as an econometric model, obtaining data, estimating the model, testing hypotheses, making forecasts, and using the model for policy purposes. Regression analysis is a key tool in econometrics that relates a dependent variable to one or more independent variables, with an error term included to account for the inexact nature of economic relationships.
This document discusses the use of dummy variables in econometric modeling. It begins by explaining that some variables cannot be quantified numerically and provides examples where dummy variables would be used. It then discusses how dummy variables are incorporated into regression models, including intercept dummy variables, slope dummy variables, and dummy variables for multiple categories. The document also covers seasonal dummy variables and concludes by explaining the Chow test and dummy variable test for testing structural stability using dummy variables.
Unit Root Test
To explain the concept of Unit root test
To highlight the different names of unit root test
Explaining the, Dickey Fuller unit root test Augmented Dickey Fuller test Phillips -Perron test
Unit Root Test
1: What is unit root?
2: How to check unit root?
3: Types of unit root test
4: Dickey fuller
5: Augmented dickey fuller
6: Phillip perron
7: Testing Unit Root on E-views
This document provides instructions for analyzing economic data. The first step is to describe the data by examining the moments of the distribution and creating some graphs. This allows you to understand the characteristics of the data set. Once the data is described, you are ready to begin regression analysis to understand relationships between variables.
The document discusses the decline in law school applicants over the past decade. Between 2010-2011 and 2013, law school applicants progressively declined. The acceptance rate for law schools increased from 55.6% in 2004 to 76.9% in 2013 as schools attempted to match the reduction in demand with a reduction in supply. Prestigious law schools still have high employment rates around 95% concentrated in large law firms, while most other law schools have employment rates of 80% or above.
This document contains analysis of stationarity and unit root tests for the S&P 500 Index (SPIndex) and Atlanta housing price index (AtlantaHPIndex) time series data. Optimal lags were selected using the Bayesian information criterion. Unit root tests using these lags show that the null hypothesis of non-stationarity cannot be rejected for the SPIndex, but can be rejected for the AtlantaHPIndex, indicating it is stationary.
Este documento presenta una introducción al modelo de regresión lineal múltiple. Explica que este modelo utiliza más de una variable explicativa para predecir los valores de una variable dependiente de manera más precisa que la regresión lineal simple. Además, describe cómo se estiman los parámetros del modelo mediante el método de mínimos cuadrados y cómo se evalúa la bondad de ajuste del modelo. Finalmente, introduce un ejemplo para ilustrar la aplicación de la regresión lineal múltiple.
The document discusses unrestricted vector autoregression (VAR) models. It analyzes a VAR model using quarterly data on H6 money aggregate DDA, personal income, and 10-year Treasury rates from the early 1960s to 2015. The model includes endogenous and exogenous variables. The main benefits of VAR discussed are that it allows measuring the impact of shocks to endogenous variables on other variables using impulse response functions and forecast error variance decompositions. However, the document notes some limitations of VAR models and questions whether some results like impulse responses truly represent economic relationships.
The document discusses deep learning and deep neural networks. Some key points:
1) A deep neural network (DNN) has at least two hidden layers, whereas a regular neural network only has one hidden layer. DNNs can be thought of as a series of logit regressions with intermediate factors representing hidden layers.
2) Important parameters for DNNs include the number of hidden layers, number of nodes per layer, activation functions, number of iterations, and output function. Tuning these parameters is important.
3) The author tested various DNN structures on a dataset to predict stock market returns, comparing performance to a regression model. DNN models with one hidden layer of 5-7 nodes performed better than the regression
Quantitative method intro variable_levels_measurementKeiko Ono
This document discusses variables, levels of measurement, and key terms in quantitative methods. It defines a variable as a property of an observation that can take on two or more values. There are three levels of measurement for variables: nominal, ordinal, and interval. Nominal variables categorize without order, ordinal can be ordered but differences are not exact, and interval variables have exact differences represented by each value. Appropriate summary statistics depend on the level of measurement, with nominal only allowing frequency and mode, ordinal adding median and range, and interval permitting all including mean, variance, minimum, and maximum.
Lecture notes on Johansen cointegrationMoses sichei
This document discusses the Johansen cointegration procedure and error correction models. It provides an example where there are 3 variables (short-term interest rate, 3-year interest rate, and 10-year interest rate) that are cointegrated with 2 cointegrating relationships. The error correction form of the vector autoregression is shown, with the 2 cointegrating vectors entering each equation. Restrictions can be tested on the coefficients of the cointegrating vectors (beta) using likelihood ratio tests. This allows testing of economic theory restrictions on the long-run relationships between the variables.
This document provides an outline and overview of tutorials for using the STATA data analysis software. It describes STATA's capabilities for data management tasks like sorting, keeping/dropping variables and observations, merging datasets, and working with dates. It lists example datasets from an econometrics textbook that are used in the tutorials. The website www.STATA.org.uk hosts step-by-step screenshot guides for various STATA functions covering data management, statistical analysis, importing data, and more.
These days a lot of data being generated is in the form of time series. From climate data to users post in social media, stock prices, neurological data etc. Discovering the temporal dependence between different time series data is important task in time series analysis. It finds its application in varied fields ranging from advertising in social media, finding influencers, marketing, share markets, psychology, climate science etc. Identifying the networks of dependencies has been studied in this report.
In this report we have study how this problem has been studied in the field of econometrics. We will also study three different approaches for building causal networks between the time series and then see how this knowledge has been used in three completely different fields. At last some important issues are presented and areas in which this can be extended for further research.
Issues associated with Unit Root, multicollinearity, and autocorrelation. Those issues are not as black-and-white as people think they are. They are rather complex and at times even inconclusive. Read why.
This document outlines the generalised method of moments (GMM) estimation technique. It begins with the basic principles of GMM, including that it uses theoretical relations that parameters should satisfy to choose parameter estimates. It then discusses estimating GMM, hypothesis testing with GMM, and extensions such as using GMM with dynamic stochastic general equilibrium (DSGE) models. The document provides details on how population moments relate to sample moments, and how method of moments estimation and instrumental variables estimation can both be viewed as special cases of GMM. It concludes by explaining how the generalized method of moments estimator works by minimizing a weighted distance between sample and population moments.
The document discusses various econometric modeling techniques including regression equations, cointegration, error correction models, vector autoregressive (VAR) modeling, and vector error correction models (VECM). It explains that regression equations can produce spurious results if the data is non-stationary, and that cointegration exists if the residuals from a regression equation are stationary. Error correction models specify the short-run relationship that maintains the long-run equilibrium between cointegrated variables. VAR models express current values of variables as functions of past values, while VECMs are VARs in first differences that incorporate the long-run cointegrating relationships between variables.
This document is an introduction to statistical machine learning presented by Christfried Webers from NICTA and The Australian National University. It discusses linear basis function models and how to perform maximum likelihood and least squares estimation. Specifically, it shows that maximizing the likelihood is equivalent to minimizing the sum-of-squares error, and that the maximum likelihood solution is given by the pseudo-inverse of the design matrix. It also examines the geometry of least squares and the bias-variance decomposition.
This document discusses Granger causality and how to test for it. It provides the following key points:
1) Granger causality measures whether variable A occurs before variable B and helps predict B, but does not guarantee true causality. If A does not Granger cause B, one can be more confident A does not cause B.
2) To test for Granger causality, autoregressive models are developed with and without the variable being tested, and an F-test or t-test is used to see if adding the variable significantly lowers the residuals.
3) The document applies this to test if changes in loans Granger cause changes in deposits using quarterly U.S. financial
This document discusses multiple linear regression analysis. It begins by defining a multiple regression equation that describes the relationship between a response variable and two or more explanatory variables. It notes that multiple regression allows prediction of a response using more than one predictor variable. The document outlines key elements of multiple regression including visualization of relationships, statistical significance testing, and evaluating model fit. It provides examples of interpreting multiple regression output and using the technique to predict outcomes.
The document discusses discretionary fiscal policy during recessions caused by fundamental shocks that lower the steady state level of production and consumption. It argues that households, being risk averse and non-cooperative, will not "jump" their consumption to the new efficient path but instead follow a Pareto inefficient path with continuously falling consumption. The best policy option in this scenario is for the government to increase consumption through fiscal policy until demand meets current production levels, maintaining this for a long period. This will lead to large government debts but they can be sustained if taxes are appropriately increased in the future.
This document discusses whether governments should intervene fiscally during recessions and how. It analyzes a recession caused by an upward shift in the rate of time preference (RTP), which changes the steady state downward. Households would normally "jump" consumption to the new steady state path to maintain efficiency, but they prefer smooth consumption. As a result, they choose an inefficient path with high unemployment. The government has three options: do nothing, increase spending, or cut taxes. Increasing spending to match demand with current production for a long period is argued to be the best approach, despite large accumulating debts that can be sustained through future tax increases.
Munich Personal RePEc ArchiveShould a Government Fiscally .docxroushhsiu
Munich Personal RePEc Archive
Should a Government Fiscally Intervene
in a Recession and, If So, How?
Harashima, Taiji
Kanazawa Seiryo University
2 April 2017
Online at https://mpra.ub.uni-muenchen.de/78053/
MPRA Paper No. 78053, posted 31 Mar 2017 09:03 UTC
Should a Government Fiscally Intervene in a Recession and, If So, How?
Taiji HARASHIMA*
April, 2017
Abstract
The validity of discretionary fiscal policy in a recession will differ according to the cause and
mechanism of recession. In this paper, discretionary fiscal policy in a recession caused by a
fundamental shock that changes the steady state downwards is examined. In such a recession,
households need to discontinuously increase consumption to a point on the saddle path to
maintain Pareto efficiency. However, they will not “jump” consumption in this manner and
instead will choose a “Nash equilibrium of a Pareto inefficient path” because they dislike
unsmooth and discontinuous consumption and behave strategically. The paper concludes that
increasing government consumption until demand meets the present level of production and
maintaining this fiscal policy for a long period is the best option. Consequent government debts
can be sustainable even if they become extremely large.
JEL Classification code: E20, E32, E62, H20, H30, H63
Keywords: Discretionary Fiscal policy; Recession; Government consumption; Government
debts; Pareto inefficiency; Time preference
*Correspondence: Taiji HARASHIMA, Kanazawa Seiryo University, 10-1 Goshomachi-Ushi,
Kanazawa-shi, Ishikawa, 920-8620, Japan.
Email: [email protected] or [email protected]
mailto:[email protected]
mailto:[email protected]
1
1 INTRODUCTION
Discretionary fiscal policy has been studied from many perspectives since the era of Keynes
(e.g., Keynes, 1936; Kopcke et al., 2006; Chari et al., 2009; Farmer, 2009; Alesina, 2012;
Benhabib et al., 2014). An important issue is whether a government should intervene fiscally in
a recession, and if so, how. The answer will differ according to the cause and mechanism of
recession. Particularly, it will be different depending on whether “disequilibrium” is generated.
The concept of disequilibrium is, however, controversial and therefore arguments continue even
now about the use of discretionary fiscal policy in a recession. In this paper, the concept of
disequilibrium is not used, but instead the concept of a “Nash equilibrium of a Pareto inefficient
path” is used.
Recessions are generated by various shocks (e.g., Rebelo, 2005; Blanchard, 2009;
Ireland, 2011; Schmitt-Grohé and Uribe, 2012; McGrattan and Prescott, 2014; Hall, 2016).
Some fundamental shocks will change the steady state, and if the steady state is changed
downwards (i.e., to lower levels of production and consumption), households must change the
consumption path to one that d ...
Munich Personal RePEc ArchiveShould a Government Fiscally .docxjacmariek5
Munich Personal RePEc Archive
Should a Government Fiscally Intervene
in a Recession and, If So, How?
Harashima, Taiji
Kanazawa Seiryo University
2 April 2017
Online at https://mpra.ub.uni-muenchen.de/78053/
MPRA Paper No. 78053, posted 31 Mar 2017 09:03 UTC
Should a Government Fiscally Intervene in a Recession and, If So, How?
Taiji HARASHIMA*
April, 2017
Abstract
The validity of discretionary fiscal policy in a recession will differ according to the cause and
mechanism of recession. In this paper, discretionary fiscal policy in a recession caused by a
fundamental shock that changes the steady state downwards is examined. In such a recession,
households need to discontinuously increase consumption to a point on the saddle path to
maintain Pareto efficiency. However, they will not “jump” consumption in this manner and
instead will choose a “Nash equilibrium of a Pareto inefficient path” because they dislike
unsmooth and discontinuous consumption and behave strategically. The paper concludes that
increasing government consumption until demand meets the present level of production and
maintaining this fiscal policy for a long period is the best option. Consequent government debts
can be sustainable even if they become extremely large.
JEL Classification code: E20, E32, E62, H20, H30, H63
Keywords: Discretionary Fiscal policy; Recession; Government consumption; Government
debts; Pareto inefficiency; Time preference
*Correspondence: Taiji HARASHIMA, Kanazawa Seiryo University, 10-1 Goshomachi-Ushi,
Kanazawa-shi, Ishikawa, 920-8620, Japan.
Email: [email protected] or [email protected]
mailto:[email protected]
mailto:[email protected]
1
1 INTRODUCTION
Discretionary fiscal policy has been studied from many perspectives since the era of Keynes
(e.g., Keynes, 1936; Kopcke et al., 2006; Chari et al., 2009; Farmer, 2009; Alesina, 2012;
Benhabib et al., 2014). An important issue is whether a government should intervene fiscally in
a recession, and if so, how. The answer will differ according to the cause and mechanism of
recession. Particularly, it will be different depending on whether “disequilibrium” is generated.
The concept of disequilibrium is, however, controversial and therefore arguments continue even
now about the use of discretionary fiscal policy in a recession. In this paper, the concept of
disequilibrium is not used, but instead the concept of a “Nash equilibrium of a Pareto inefficient
path” is used.
Recessions are generated by various shocks (e.g., Rebelo, 2005; Blanchard, 2009;
Ireland, 2011; Schmitt-Grohé and Uribe, 2012; McGrattan and Prescott, 2014; Hall, 2016).
Some fundamental shocks will change the steady state, and if the steady state is changed
downwards (i.e., to lower levels of production and consumption), households must change the
consumption path to one that d.
Munich Personal RePEc ArchiveShould a Government Fiscally .docxdohertyjoetta
Munich Personal RePEc Archive
Should a Government Fiscally Intervene
in a Recession and, If So, How?
Harashima, Taiji
Kanazawa Seiryo University
2 April 2017
Online at https://mpra.ub.uni-muenchen.de/78053/
MPRA Paper No. 78053, posted 31 Mar 2017 09:03 UTC
Should a Government Fiscally Intervene in a Recession and, If So, How?
Taiji HARASHIMA*
April, 2017
Abstract
The validity of discretionary fiscal policy in a recession will differ according to the cause and
mechanism of recession. In this paper, discretionary fiscal policy in a recession caused by a
fundamental shock that changes the steady state downwards is examined. In such a recession,
households need to discontinuously increase consumption to a point on the saddle path to
maintain Pareto efficiency. However, they will not “jump” consumption in this manner and
instead will choose a “Nash equilibrium of a Pareto inefficient path” because they dislike
unsmooth and discontinuous consumption and behave strategically. The paper concludes that
increasing government consumption until demand meets the present level of production and
maintaining this fiscal policy for a long period is the best option. Consequent government debts
can be sustainable even if they become extremely large.
JEL Classification code: E20, E32, E62, H20, H30, H63
Keywords: Discretionary Fiscal policy; Recession; Government consumption; Government
debts; Pareto inefficiency; Time preference
*Correspondence: Taiji HARASHIMA, Kanazawa Seiryo University, 10-1 Goshomachi-Ushi,
Kanazawa-shi, Ishikawa, 920-8620, Japan.
Email: [email protected] or [email protected]
mailto:[email protected]
mailto:[email protected]
1
1 INTRODUCTION
Discretionary fiscal policy has been studied from many perspectives since the era of Keynes
(e.g., Keynes, 1936; Kopcke et al., 2006; Chari et al., 2009; Farmer, 2009; Alesina, 2012;
Benhabib et al., 2014). An important issue is whether a government should intervene fiscally in
a recession, and if so, how. The answer will differ according to the cause and mechanism of
recession. Particularly, it will be different depending on whether “disequilibrium” is generated.
The concept of disequilibrium is, however, controversial and therefore arguments continue even
now about the use of discretionary fiscal policy in a recession. In this paper, the concept of
disequilibrium is not used, but instead the concept of a “Nash equilibrium of a Pareto inefficient
path” is used.
Recessions are generated by various shocks (e.g., Rebelo, 2005; Blanchard, 2009;
Ireland, 2011; Schmitt-Grohé and Uribe, 2012; McGrattan and Prescott, 2014; Hall, 2016).
Some fundamental shocks will change the steady state, and if the steady state is changed
downwards (i.e., to lower levels of production and consumption), households must change the
consumption path to one that d.
Munich Personal RePEc ArchiveShould a Government Fiscally .docxgriffinruthie22
Munich Personal RePEc Archive
Should a Government Fiscally Intervene
in a Recession and, If So, How?
Harashima, Taiji
Kanazawa Seiryo University
2 April 2017
Online at https://mpra.ub.uni-muenchen.de/78053/
MPRA Paper No. 78053, posted 31 Mar 2017 09:03 UTC
Should a Government Fiscally Intervene in a Recession and, If So, How?
Taiji HARASHIMA*
April, 2017
Abstract
The validity of discretionary fiscal policy in a recession will differ according to the cause and
mechanism of recession. In this paper, discretionary fiscal policy in a recession caused by a
fundamental shock that changes the steady state downwards is examined. In such a recession,
households need to discontinuously increase consumption to a point on the saddle path to
maintain Pareto efficiency. However, they will not “jump” consumption in this manner and
instead will choose a “Nash equilibrium of a Pareto inefficient path” because they dislike
unsmooth and discontinuous consumption and behave strategically. The paper concludes that
increasing government consumption until demand meets the present level of production and
maintaining this fiscal policy for a long period is the best option. Consequent government debts
can be sustainable even if they become extremely large.
JEL Classification code: E20, E32, E62, H20, H30, H63
Keywords: Discretionary Fiscal policy; Recession; Government consumption; Government
debts; Pareto inefficiency; Time preference
*Correspondence: Taiji HARASHIMA, Kanazawa Seiryo University, 10-1 Goshomachi-Ushi,
Kanazawa-shi, Ishikawa, 920-8620, Japan.
Email: [email protected] or [email protected]
mailto:[email protected]
mailto:[email protected]
1
1 INTRODUCTION
Discretionary fiscal policy has been studied from many perspectives since the era of Keynes
(e.g., Keynes, 1936; Kopcke et al., 2006; Chari et al., 2009; Farmer, 2009; Alesina, 2012;
Benhabib et al., 2014). An important issue is whether a government should intervene fiscally in
a recession, and if so, how. The answer will differ according to the cause and mechanism of
recession. Particularly, it will be different depending on whether “disequilibrium” is generated.
The concept of disequilibrium is, however, controversial and therefore arguments continue even
now about the use of discretionary fiscal policy in a recession. In this paper, the concept of
disequilibrium is not used, but instead the concept of a “Nash equilibrium of a Pareto inefficient
path” is used.
Recessions are generated by various shocks (e.g., Rebelo, 2005; Blanchard, 2009;
Ireland, 2011; Schmitt-Grohé and Uribe, 2012; McGrattan and Prescott, 2014; Hall, 2016).
Some fundamental shocks will change the steady state, and if the steady state is changed
downwards (i.e., to lower levels of production and consumption), households must change the
consumption path to one that d.
Munich Personal RePEc ArchiveShould a Government Fiscally .docxgemaherd
Munich Personal RePEc Archive
Should a Government Fiscally Intervene
in a Recession and, If So, How?
Harashima, Taiji
Kanazawa Seiryo University
2 April 2017
Online at https://mpra.ub.uni-muenchen.de/78053/
MPRA Paper No. 78053, posted 31 Mar 2017 09:03 UTC
Should a Government Fiscally Intervene in a Recession and, If So, How?
Taiji HARASHIMA*
April, 2017
Abstract
The validity of discretionary fiscal policy in a recession will differ according to the cause and
mechanism of recession. In this paper, discretionary fiscal policy in a recession caused by a
fundamental shock that changes the steady state downwards is examined. In such a recession,
households need to discontinuously increase consumption to a point on the saddle path to
maintain Pareto efficiency. However, they will not “jump” consumption in this manner and
instead will choose a “Nash equilibrium of a Pareto inefficient path” because they dislike
unsmooth and discontinuous consumption and behave strategically. The paper concludes that
increasing government consumption until demand meets the present level of production and
maintaining this fiscal policy for a long period is the best option. Consequent government debts
can be sustainable even if they become extremely large.
JEL Classification code: E20, E32, E62, H20, H30, H63
Keywords: Discretionary Fiscal policy; Recession; Government consumption; Government
debts; Pareto inefficiency; Time preference
*Correspondence: Taiji HARASHIMA, Kanazawa Seiryo University, 10-1 Goshomachi-Ushi,
Kanazawa-shi, Ishikawa, 920-8620, Japan.
Email: [email protected] or [email protected]
mailto:[email protected]
mailto:[email protected]
1
1 INTRODUCTION
Discretionary fiscal policy has been studied from many perspectives since the era of Keynes
(e.g., Keynes, 1936; Kopcke et al., 2006; Chari et al., 2009; Farmer, 2009; Alesina, 2012;
Benhabib et al., 2014). An important issue is whether a government should intervene fiscally in
a recession, and if so, how. The answer will differ according to the cause and mechanism of
recession. Particularly, it will be different depending on whether “disequilibrium” is generated.
The concept of disequilibrium is, however, controversial and therefore arguments continue even
now about the use of discretionary fiscal policy in a recession. In this paper, the concept of
disequilibrium is not used, but instead the concept of a “Nash equilibrium of a Pareto inefficient
path” is used.
Recessions are generated by various shocks (e.g., Rebelo, 2005; Blanchard, 2009;
Ireland, 2011; Schmitt-Grohé and Uribe, 2012; McGrattan and Prescott, 2014; Hall, 2016).
Some fundamental shocks will change the steady state, and if the steady state is changed
downwards (i.e., to lower levels of production and consumption), households must change the
consumption path to one that d.
Munich personal re p ec archiveshould a government fiscally MARRY7
- The document examines whether a government should intervene fiscally during a recession caused by an upward shift in the rate of time preference (RTP).
- When the RTP increases, the steady state of the economy shifts downward, requiring households to adjust their consumption path. However, households will not "jump" consumption to the new efficient path due to risk aversion and wanting smooth consumption over time.
- This results in households choosing a "Nash equilibrium of a Pareto inefficient path" rather than the efficient path, leading to increased unemployment during the recession. The document evaluates whether the government should intervene through increasing spending, cutting taxes, or not intervening at all.
This document discusses trends and reversals in financial markets and the economy. It notes that while trend reversals create fear, they are natural and inevitable. The key is understanding the dominant trend versus short-term volatility. It also discusses how central banks have intervened in markets to smooth economic cycles, but that their policies may no longer be appropriate given high debt levels and slower growth. While recent trend reversals in markets have been correlated, the document argues they will likely revert back to the dominant downward trend.
This document summarizes a research paper that develops a dynamic stochastic general equilibrium (DSGE) model to explain how monetary policy affects risk in financial markets and the macroeconomy. The key feature of the model is that asset and goods markets are segmented because it is costly for households to transfer funds between the markets. The model generates endogenous movements in risk as the fraction of households that rebalance their portfolios varies over time in response to real and monetary shocks. Simulation results indicate the model can account for evidence that monetary policy easing reduces equity premiums and helps explain the response of stock prices to monetary shocks.
- A liquidity trap is a situation where the short-term nominal interest rate is zero, meaning that increasing the money supply has no effect on output or prices according to traditional Keynesian theory.
- Modern theory argues that monetary policy can still be effective even at zero interest rates by managing expectations about future money supply levels when interest rates rise above zero again.
- For monetary policy to be effective in a liquidity trap, central banks must commit to maintaining lower future interest rates once deflationary shocks subside in order to stimulate expectations about future money supply levels and interest rates.
In a recessionary and deflationary framework, the discretionary monetary policy cannot be optimal when the interest rate is already near zero and cannot decrease anymore. Indeed, when the Zero Lower Bound is binding, a negative demand shock implies a decrease in the current economic activity level and deflationary tensions, which cannot be avoided by monetary policy as the nominal interest rate can no longer decrease. The economic literature has then often recommended to target an inflation rate sufficiently above zero in order to avoid the dangers of this Zero Lower Bound (ZLB) constraint. On the contrary, provided the ZLB is not binding, monetary policy can efficiently contribute to the stabilization of economic activity and inflation in case of demand shocks. The variation in interest rates is then all the more accentuated as interest rate smoothing is a more negligible goal for the central bank. The contribution of our paper is to provide a clear analytical New-Keynesian framework sustaining these results. Besides, our analytical modelling also shows that even if the ZLB is currently not binding, the central bank should take into account the dangers of a potential future binding ZLB. Indeed, the interest rate should be decreased the fastest as a negative demand shock and the possibility to reach the ZLB is anticipated for a nearest future period. Our paper demonstrates the necessity of such a ‘pre-emptive’ active monetary policy even in a discretionary framework, which has the advantage to be time-consistent and to be in conformity with the empirical practices of independent central banks. We don’t have to make the strong hypothesis of a commitment monetary policy intended to affect private agents’ expectations in order to demonstrate the optimality of such a pre-emptive monetary policy.
Question 1Response 1Development inside and out effects t.docxaudeleypearl
Question 1:
Response 1:
Development inside and out effects the entire country's economy. It impacts the managing body, regardless the clearly irrelevant subtleties in the average person's dependably life. Both a conditions and clear deferred results of how the economy is getting along, swelling has the two its fans and spoilers. Distinctive envisions that particular degrees of swelling are helpful for a prospering economy, yet that progressively critical rates raise concerns. It can degrade the money basically and, at logically lamentable, has been a key part to subsidences.
Swelling, as referenced, is the rate a worth ascensions, and fundamentally how much the dollar is worth at a given moment concerning checking. The idea behind swelling being an impact for good in the economy is that a reasonable enough rate can nudge financial movement without debasing the money so much that it ends up being basically vain (Kohn, 2006).
Swelling can in like manner falter from asset for asset. Subordinate upon the season, the expense of gas could go up independently from with everything considered headway as it routinely does as summer moves close. In reality, there is even a term - focus improvement - for swelling that parts in everything except for sustenance and imperativeness (gas and oil), as these regions have separate factors that add to them. There are a wide degree of sorts of swelling, subordinate upon what remarkable is being viewed comparatively as what the development rate truly is by all accounts. For example, what happens if the swelling rate is well over the Fed's normal goal? At a higher rate, yet still in the single digits, that is known as walking swelling. It is seen as concerning yet sensible (Ball, 2006).
Swelling is generally depicted reliant on its rate and causes. By and large, Inflation happens in an economy when vitality for thing and experiences outmaneuvers the supply of yield. in this manner, clarifications behind Inflation have different sides, the intrigue side and supply side. The widely inclusive activity of hazard premiums in driving enlargement pay over the scope of advancing years is dependable with secured budgetary improvement and inside and out oblige cash related procedure events in the moved economies. The degree for further fitting budgetary enabling seen with money related stars seems to have declined amidst the enough low advance charges and gigantic monetary records of national banks (Bodie, 2016).
In relentless time, the correspondence of perils has wound up being constantly phenomenal, the general point of view has lit up, and money related conditions have engaged on net. With the work superstar proceeding to reinforce, and GDP improvement expected to keep up a vital good ways from back in the consequent quarter, it likely will be fitting soon to change the affiliation supports rate. Likewise, if the economy propels as shown by the SEP concentrate way, the affiliation supports rate will probably app ...
Module 4 - SLPRESPONSIBILITY ACCOUNTING FOR COST, PROFIT AND INV.docxroushhsiu
Module 4 - SLP
RESPONSIBILITY ACCOUNTING FOR COST, PROFIT AND INVESTMENT CENTERS
The following data pertain to the operating revenues and expenses for California, Inc. for 20XX.
Los Angeles
(LA) Segment
San Francisco
(SF) Segment
Total
Sales
$180,000
$360,000
$540,000
Variable expenses
96,000
240,000
336,000
Direct fixed expenses
24,000
30,000
54,000
Indirect fixed expenses
72,000
Assets (investment) used to generate operating income for the two segments are shown below.
Los Angeles
Segment
San Francisco
Segment
Assets directly used by and identified with the segment
$180,000
$360,000
a. Prepare a segmented income statement in good format showing the contribution margin of each segment, the contribution to indirect expenses of each segment, and the total income of California, Inc.
b. Determine the return on investment for evaluating (1) the earning power of the entire company and (2) the performance of each segment.
c. Comment on the results of part (b).
SLP Assignment Expectations
Show computations in good format and explain answers as required. Excel is a great tool to make computations and present financial information in an easy to understand format. Write comments below the computations in Excel. Submit only the Excel file. Both content and the clarity of the presentation will be evaluated for grading purposes.
MPRA
Munich Personal RePEc Archive
Should a Government Fiscally Intervene
in a Recession and, If So, How?
Taiji Harashima
Kanazawa Seiryo University
2 April 2017
Online at https://mpra.ub.uni-muenchen.de/78053/
MPRA Paper No. 78053, posted 31 March 2017 09:03 UTC
http://mpra.ub.uni-muenchen.de/
https://mpra.ub.uni-muenchen.de/78053/
Should a Government Fiscally Intervene in a Recession and, If So, How?
Taiji HARASHIMA*
April, 2017
Abstract
The validity of discretionary fiscal policy in a recession will differ according to the cause and
mechanism of recession. In this paper, discretionary fiscal policy in a recession caused by a
fundamental shock that changes the steady state downwards is examined. In such a recession,
households need to discontinuously increase consumption to a point on the saddle path to
maintain Pareto efficiency. However, they will not “jump” consumption in this manner and
instead will choose a “Nash equilibrium of a Pareto inefficient path” because they dislike
unsmooth and discontinuous consumption and behave strategically. The paper concludes that
increasing government consumption until demand meets the present level of production and
maintaining this fiscal policy for a long period is the best option. Consequent government debts
can be sustainable even if they become extremely large.
JEL Classification code: E20, E32, E62, H20, H30, H63
Keywords: Discretionary Fiscal policy; Recession; Government consumption; Government
debts; Pareto inefficiency; Time preference
*Correspon ...
This document summarizes a study that modeled volatility and daily exchange rate movement between the Nigerian naira and US dollar from January 2001 to May 2019. The results found that exchange rate volatility is positively related to returns and persistent over time. It was also discovered that negative news produces more volatility than positive news of equal magnitude, indicating an asymmetric or "leverage" effect. The researchers recommend that the Central Bank of Nigeria intervene more actively to reduce excess volatility between the currencies.
This document discusses the mathematics behind the Phillips curve and different monetary policy approaches. It summarizes an initial model of the Phillips curve that assumes expected inflation equals actual inflation and a constant monetary policy by the central bank. This initial model results in periodic behavior of unemployment and inflation. The document then introduces the idea of a variable monetary policy where the central bank adapts its policy based on unemployment and inflation levels. This makes the relationship between monetary policy and the Phillips curve nonlinear.
Reserving in High Inflation.
General Considerations by Alejandra Nolibos
Specific example by Alejandro Ortega
Presented at CLRS in Atlanta, September 11,2015
Argentina Auto Case Study
The paper examines how real exchange rate volatility affects productivity growth and how this relationship depends on a country's level of financial development. It finds that the impact of exchange rate volatility on growth varies according to a country's financial development - volatility has a more negative impact in countries with lower financial development. The authors develop a theoretical model to explain this finding, where financial development determines firms' ability to finance innovation costs during exchange rate fluctuations. However, some comments note issues with the empirical specifications and question aspects of linking the model to the empirical results.
The document discusses intermarket analysis, which examines the correlations between stocks, bonds, commodities, and currencies. In inflationary environments, stocks and bonds are positively correlated, while bonds and commodities and the US dollar and commodities are inversely correlated. In deflationary environments, stocks and bonds become inversely correlated. Understanding these intermarket relationships can help analysts determine the stage of the economic cycle and select appropriate sectors to invest in.
This document discusses the concept of "black swans" and economic forecasting. It begins by explaining the origin of the term "black swan" and how Nassim Taleb later used it to describe rare events with disproportionate impacts. It then discusses challenges with economic analysis and forecasting due to lack of data and uncertainties. The rest of the document focuses on analyzing past recessions and economic cycles, challenges with the recent recovery, issues around credit growth and deleveraging, and the importance of considering many interrelated factors when developing economic forecasts. It also describes the machine learning techniques and models used by the company discussed in the document to generate their economic forecasts.
Similar to Structural breaks, unit root tests and long time series (20)
Partnership with a Premier Business SchoolArup Guha
Our partnership with a premier business school in Delhi has grown for over 6 years. What started with helping in business research curriculum design has expanded to participation in almost all analytical content design and training. It is our most valuable relationship since apart from monetary benefits it also helps us develop our knowledge
Logistic regression for ordered dependant variable with more than 2 levelsArup Guha
This document discusses multinomial logistic regression models. Multinomial logistic regression can handle dependent variables with more than two categories that may be ordinal (ordered categories) or nominal (unordered categories). The document focuses on proportional odds cumulative logit models, which model ordinal dependent variables by considering the natural ordering of categories. It provides an example of using SAS code to fit a proportional odds model to model the impact of radiation exposure on human health.
Survival analysis can provide three useful outputs: 1) the chances of surviving to a particular time period, 2) the expected lifetime of individuals or groups, and 3) the chances of an event occurring within a specific interval, given it has not occurred already. The most detailed information is a table showing expected time to event for all possible individual profiles and events of interest. This allows determining which groups require more urgent treatment or have higher insurance premiums based on their expected survival times. While survival analysis alone cannot determine appropriate treatments, it can measure treatment effectiveness by comparing survival times after treatment.
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The document discusses different approaches to measuring the effectiveness of advertisements, including recognition, recall, and favorability. Recognition and recall-based metrics were commonly used but are now considered inadequate because they do not capture indirect or implicit effects of ads. More recent approaches emphasize measuring favorability and implicit memory effects, even from ads that are not directly remembered. The document analyzes shortcomings of older approaches and advocates enhancing the questionnaire to include measures of proper recall and favorability to more accurately measure ad effectiveness.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
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Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
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Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
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2. Macro economic variables consist of GNP, unemployment, inflation, interest
rate, exchange rate, balance of payments, etc.
Unacceptable levels (think high inflation) or instability (think alternating
periods of high and low growth) in any of the above variables can be very
distressing for the people (think prices increasing by 20% every month
OR being fired from your job due to low revenue growth prospects of
your employer due to recession)
3. Hyperinflation in Zimbabwe:
! From NewYorkTimes, “at a supermarket near the centre of
this tatterdemalion capital, toilet paper costs $417. No, not
per roll. Four hundred seventeen Zimbabwean dollars is the
value of a single two-ply sheet. A roll costs $145,750 — in
American currency, about 69 cents.The price of toilet paper,
like everything else here, soars almost daily, spawning jokes
about an impending better use for Zimbabwe's $500 bill,
now the smallest in circulation.”
! 100Trillion Zimbabwean dollar bills were in circulation
! In November 2008, inflation hit a high of 79.6 billion percent.
So
$Z 79600000000 would have to be paid in Nov 2008 for a
pen priced at $Z1 in Nov 2007
4. The Great Recession (2007-2009)
1. US unemployment rate rose from 5% in 2008 pre-crisis to 10%
by late 2009
2. Post recession Income levels of the median male worker was
down to 1968 levels
3. Approximately 5.4 million people have been added to federal
disability rolls
4. U.S. total national debt rose from 66% GDP in 2008 pre-crisis to
over 103% by the end of 2012
5. Macro variables reflect different aspects of the same economy so they
are interconnected and fluctuations in one can quickly translate into
fluctuations in the others
Consider this situation: When inflation is high, people may lose confidence in money
as the real value of savings is severely reduced
This discourages savings due to the fact that the money is worth more presently
than in the future
This expectation reduces economic growth because the economy needs a certain
level of savings to finance investments which boosts economic growth.
Also, inflation makes it harder for businesses to plan for the future. It is very
difficult to decide how much to produce, because businesses cannot predict the
demand for their product at the higher prices they will have to charge in order to
cover their costs.
Savings
Growth
Investment
6. To stabilize the economy over time, governments
need to formulate policy to control the macro
variables for which they need to understand the
long term relation between them. Is this possible?
The most powerful tool to understand the relation
between crucial macro variables is Time Series
Analysis: a branch of Econometrics or statistical
analysis of economic variables
7. • Nelson and Plosser (1982) argued that almost all macroeconomic time
series, have a unit root
What does this mean:
• In the absence of unit root (stationary), the series fluctuates around a constant long-
run mean and implies that the series has a finite variance which does not depend on
time.
• On the other hand, non-stationary series have no tendency to return to long-run
deterministic path and the variance of the series is time dependent.
• Non-stationary series suffer permanent effects from random
shocks and thus the series follow a random walk
• Think tourist arrivals at a destination over time. If this series is non-stationary, then
in case of random shocks like terrorist attacks or natural disaster, the number of
tourist arrivals never revert to their original mean, but if the series was stationary
they would have.
8. • If this were true, there is no use to policy anymore.
The effects of hyperinflation or a recession on the
economy are permanent and incurable and we are
doomed to be on a low level path forever.
• Such a Greek tragedy scenario where you are
completely at the mercy of the Gods doesn’t seem
relevant for current times
9. Perron (1989), argued that in the presence of a structural
break, the standard ADF tests for unit root are biased
towards the non-rejection of the null hypothesis
The series on the left is non-stationary but the one on the
right is not.. However, ADF tests might misleadingly point
out the series on the right to be non-stationary as well
10. Testing for structural breaks is extremely important
while analyzing long time series. Otherwise, all
subsequent analysis might be misleading
For instance, two series are cointegrated if they are
individually I(1), but some vector of coefficients
exists to form a stationary linear combination of
them
However, in the presence of structural breaks,
unless proper testing is done, the individual series
might mistakenly by labeled I(1)
11. Test Model Software
Perron (1989)** Exogenous with one break
Zivot and Andrews (1992)*
Endogenous with one
break Eviews
Lumsdaine and Papell (1997)*
Endogenous with two
breaks GAUSS
Lee and Strazicich (2003)**
Endogenous with two
breaks RATS
Gregory and Hansen (1996)
One Endogenous break in
cointegration framework Eviews
Saikkonen and Lütkepohl (2000)
One Endogenous break in
cointegration framework GAUSS
Bai and Perron (2003)
Endogenous multiple
breaks RATS,Eviews
* Assume no break(s) under the null
hypothesis of unit root
** Assume break(s) under both the null and the alternative hypothesis
12. The Indian Ministry of Statistics and Program
Implementation has just introduced a structural
break in the GDP series. Read up and be careful
The tests in the earlier slide are quite technical. But
expectedly anyone interested in this issue is likely to
have a technical appetite. Hence happy reading.
Zivot & Andrews (intensely mathematical) is good
place to start
Tests for panel data are a different set. Search
Westlund, Levin-Lin-Chu, Pedroni etc.