Econometric Methods and Applications

Prof. Pami Dua is Director and Professor of Economics, Delhi School of Economics as well as Chairperson, Research Council and Dean, Academic Activities and Projects, University of Delhi. She is currently serving as a member of the Monetary Policy Committee of the Reserve Bank of India. She is also former President of the Indian Econometric Society. She obtained her Ph.D. in Economics from London School of Economics and has published widely in time series econometrics, forecasting, macroeconometrics and business cycle analysis. Prof. K.L. Krishna is currently Chairperson of Madras Institute of Development Studies. He has been leading the India KLEMS Productivity project, funded by the Reserve Bank of India, as part of the World KLEMS Initiative, since 2009. In the past, he has served as Director, Delhi School of Economics; Head, Department of Economics as well as Dean, Faculty of Social Sciences, University of Delhi. He was also Executive Director, Centre for Development Economics; Founder Managing Editor, Journal of Quantitative Economics; and President, The Indian Econometric Society. He did his Ph.D. in Economics from the University of Chicago and is an expert on econometrics, industrial economics, economics of productivity, regional inequality and empirics of trade.

Part I Basic Regression Analysis.- 1. Introduction.- 1.1 About the book.- 1.2 Steps in empirical project.- 1.3 Overview of chapters.- 1.4 List of empirical exercises in the book.- 2. Getting acquainted with data.- 2.1 Structure of data and types of measurements.- 2.2 Data analysis.- 2.2.1 Concepts underlying data analysis.- 2.2.2 Data analysis through graphical techniques.- 2.2.3 Data analysis through descriptive statistics.- 3. Classical Linear Regression Model.- 3.1 The simple and multiple regression models: Overview.- 3.1.1 Estimation using ordinary least squares.- 3.1.2 Properties of the ordinary least squares estimators.- 3.1.3 Goodness-of-fit .- 3.1.4 Hypothesis testing.- 3.2 Empirical Illustration: Housing price model.- 3.2.1 Literature review.- 3.2.2 Estimation of the model as simple regression model.- 3.2.3 Presentation and interpretation of regression results.- 3.2.4 Estimation of the model as a multiple regression model.- 3.2.5 Presentation and interpretation of regression results.- 3.2.6 Estimation of the model using GRETL.- 3.3 Empirical Illustration: Exchange rate and relative price ratio.- 3.3.1 Literature review.- 3.3.2 Estimation of the model as simple regression model.- 3.3.3 Presentation and interpretation of regression results.- 4. Dummy Variable Regression Models.- 4.1 Qualitative data and dummy variable regression model: Overview.- 4.1.1 Intercept, slope and multiplicative dummies.- 4.2 Empirical Illustration: Wage earnings as a function of education, age, experience, gender.- 4.2.1 Literature review.- 4.2.2 Estimation of the model with dummy variables.- 4.2.3 Interpreting intercept and slope dummies.- 4.2.4 Estimation of the model using GRETL.- 4.3 Empirical Illustration: Expenditure on running a school.- 4.3.1 Literature review.- 4.3.2 Estimation of the model with dummy variables.- 4.3.3 Interpreting intercept and slope dummies.- 4.4 Detecting seasonal trends in data - Empirical Illustration using efficient market hypothesis.- 4.4.1 Literature review.- 4.4.2 Estimation of the model.- 4.4.3 Presentation and interpretation of regression results.- Part II Diagnostics and Model Selection.- 5. Violations of the Classical Assumptions I: Multicollinearity.- 5.1 Detection, consequences, remedies: an overview.- 5.2 Empirical Illustration: Housing starts relations.- 5.2.1 Brief note on housing starts relations and literature review.- 5.2.2 Detection of multicollinearity.- 5.2.3 Consequences of multicollinearty.- 5.2.4 Remedies for multicollinearity.- 5.2.5 Estimation of the model using GRETL.- 6. Violations of the Classical Assumptions II: Heteroscedasticity.- 6.1 Detection, consequences, remedies: an overview.- 6.2 Empirical Illustration: Size of the Firm and Profit Ratios.- 6.2.1 Literature review.- 6.2.2 Detection of heteroscedasticity .- 6.2.3 Consequences of heteroscedasticity.- 6.2.4 Remedies for heteroscedasticity.- 6.2.5 Estimation of the model using GRETL.- 7. Violations of the Classical Assumptions III: Serial correlation.- 7.1 Detection, consequences, remedies: an overview.- 7.2 Empirical Illustration: Cobb Douglas Production Function.- 7.2.1 Brief note on Cobb Douglas function and literature review.- 7.2.2 Detection of serial correlation.- 7.2.3 Consequences of serial correlation .- 7.2.4 Treatment of serial correlation.- 7.2.5 Estimation of the model using GRETL.- 8. Functional Forms and Model Specification.- 8.1 Choice of functional forms.- 8.1.1 Linear log functional form: Engel expenditure function.- 8.1.2 Log linear functional form: GDP growth rate model.- 8.1.3 Double log functional form: Cobb Douglas production function.- 8.1.4 Polynomial functional form: Cost function.- 8.1.5 Reciprocal functional form: Philips curve.- 8.1.6 Functional form with interaction term: Consumption function.- 8.1.7 Regression through the origin: Capital asset pricing model.- 8.2 Model specification.- 8.2.1 Omission and inclusion of regressors.- 8.2.2 Measurement errors in variables.- 8.2.3 Test for misspecification.- 8.3 Approaches to choice of models.- 8.3.1 Traditional approach.- 8.3.2 Hendry's approach.- 8.3.3 Empirical application on model selection: Economic growth models.- 8.3.4 Empirical application on model selection: Housing price model.- 9. Guide to Doing an Empirical Project using Ordinary Least Squares.- 9.1 Steps in empirical project.- 9.2 Empirical Illustration: Consumption function.- 9.3 Empirical Illustration: Deficit and interest rates.- Part III Advanced Topics in Econometrics.- 10. Dynamic Econometrics Models.- 10.1Distributed lag models: an overview.- 10.1.1Kyock transformation.- 10.1.2Almon transformation.- 10.1.3Autoregressive models.- 10.1.4 Partial adjustment model.- 10.1.5Adaptive expectations model .- 10.2 Empirical Illustration: Demand for money.- 10.2.1 Brief note on money demand function and literature review.- 10.2.2 Estimation and interpretation of results.- 10.2.3 Estimation of the model using GRETL.- 11. Instrument Variable Estimation and Two Stage Least Squares Estimation.- 11.1 Instrument variable estimation of the regression model: an overview.- 11.2 Two stage least squares: overview .- 11.2.1 Single endogenous explanatory variable.- 11.2.2 Multiple endogenous explanatory variables.- 11.3 Empirical Illustration: Demand and supply for loans.- 11.3.1 Literature review.- 11.3.2 Instrument variable estimation and two stage least squares estimation of the model using GRETL.- 12. Simultaneous Equation Models.- 12.1 Simultaneous equation model: an overview.- 12.1.1 Problem of simultaneity.- 12.1.2 Problem of identification.- 12.1.3 Estimation procedure using method of indirect least squares.- 12.1.4 Estimation procedure using method of two stage least squares.- 12.2 Empirical Illustration: Macroeconomic Model.- 12.2.1 Brief note on macroeconomic model and literature review.- 12.2.2 Estimation of the model using GRETL.- 13. Topics in Time Series Econometrics.- 13.1 Stochastic processes: an overview.- 13.1.1 Stationary and non-stationary time series.- 13.1.2 Testing for unit roots.- 13.1.3 Cointegration estimation for unit root time series.- 13.2 Empirical Illustration: Closing stock prices of IBM.- 13.2.1 Literature review.- 13.2.2 Estimation of the model and interpretation of results.- 13.2.3 Estimation of the model using GRETL.- 13.3 Empirical Illustration: Term structure of interest rates.- 13.3.1 Literature review.- 13.3.2 Estimation of the model and interpretation of results.- 14. Panel Data Estimation.- 14.1 Panel Data: an overview.- 14.1.1 Fixed effects model.- 14.1.2 Random effects model.- 14.2 Empirical Illustration: Investment data of companies.- 14.2.1 Literature review.- 14.2.2 Estimation of the model and interpretation of results.- 14.2.3 Estimation of the model using GRET.- L 14.3 Empirical Illustration: Wage earnings model.- 14.3.1 Literature review.- 14.3.2 Estimation of the model and interpretation of results.- 15. Qualitative Response Models .- 15.1 Introduction to qualitative response model.- 15.2 Linear probability model.- 15.3 Logit model.- 15.4 Probit model.- 15.5 Empirical Illustration: Predicting bank failure.- 15.6 Empirical Illustration: Applications for mortgages.