Econometrics Theory and Application Syllabus for M.A. Economics

Econometrics: Theory and Application (Paper – 2) M.A. Economics Part-II

This course aims at introducing students to the quantitative aspects of various econometric theories. This is achieved through providing the students with an understanding of basic econometric theory and models. In particular, the topics covered in this course are the nature of regression analysis, single and multivariable regression analysis, assumptions of the classical regression model, econometric modeling, regression on dummy dependent variable, simultaneous equation models, and time series econometrics.  Special emphasis is placed on the application side of this course. Participants would make use of statistical soft wares to undertake regression analysis. Students after the completion of this course are expected to be comfortable in data analysis, apart from contributing to empirical research and analyzing projects.

Prerequisite for this course is intermediate-level knowledge of calculus, statistics, and economic theory.

Topic 1: Introduction

• Definition and scope of econometrics
• Econometric models vs. Statistical models
• Ingredients of econometric modeling Specification, estimation, verification or evaluation and forecasting.

Topic 2: The Classical Linear Regression Model

a) The Simple Linear Regression Model (SLRM)

• Estimation of SLRM by Ordinary Least Squares (OLS)
• Interpretation of Estimated Coefficients and their Economic Meanings.

b) The Multiple Linear Regression Model (MLRM)

• Estimation of MLR model by OLS and its assumptions
• Interpretation of estimated coefficients and their economic meanings, Computation of elasticities and standardized coefficients
• Using R2 as a measure of ‘Goodness of Fit’ and some problems with its use.

c) The General Linear Regression Model (GLRM)

• Estimation of GLRM by OLS through Matrix Approach Var-Cov matrix of estimated

d) Evaluating an Estimated Linear Regression Model

• Testing the significance of individual Testing the significance of the model as a whole.

Topic : 3 Multicollinearity

• What is Multicollinearety?
• The distinction between perfect Multicollinearety and less than perfect Multicollinearety (Multicollinearety problem).
• Consequences of Multicollinearety problem
• methods for detection of Multicollinearety problem
• Remedial measures for Multicollinearety problem

Topic: 4 Heteroskedasticity

• What is Heteroskedasticity and what are its causes?
• Consequences of Heteroskedasticity for OLS estimation.
• Methods for detection of Heteroskedasticity.
• Remedial measures for Heteroskedasticity.

Topic: 5 Autocorrelation

• What is Autocorrelation and its Causes?
• Consequences of Autocorrelation for OLS estimation.
• Methods for detection of Autocorrelation.
• Remedial measures for Autocorrelation.

Topic 6: Forecasting with a Single Equation Regression Model

• What is forecasting and what are its various types?
• Important features of a good forecast.
• Variance of unconditional forecast error.
• Variance of conditional forecast error.
• Measures for evaluating the forecasting power of a model.

Topic 7: Errors in Variables, Time as a Variable, Dummy Variables, Grouped Data, Lagged and Distributed-Lag Variables

• Errors in variables. Time as a variable.
• Dummy variables.
• Estimation from grouped data.
• Exogenous lagged variables.
• Endogenous lagged variables.
• Methods of estimation of lagged models.

Topic 8: Identification

• The problems of identification.
• Implications of the identification state of a model.
• Formal rules for identification.
• Identifying restrictions.
• Tests for identifying restrictions.
• Identification and Multicollinearity.

Topic 9: Simultaneous Equations Models

• Why Simultaneous Equation Models?
• Various Types of simultaneous equation Models.
• The identification problem.
• Checking the identification state of a model or of a particular equation in the model by Order Condition, bogus equation and reduced from approaches.
• Identification and methods of estimation.

Topic 10: Consistent Estimation of Parameters of Simultaneous Equations Models

• Indirect Least Square Estimation.
• Two-stage Least Squares Estimation.
• Instrumental variables method of estimation.

Topic 11: Varying Coefficient Models:

• Causes of Coefficient Variation.
• Randomly Varying Coefficient Models.
• Systematically Varying Coefficient Models.

Topic 12: Time Series Econometrics

• ARIMA Models.
• Comparison of forecasts based on ARIMA and Regression Models.
• Unit Roots and Co-integration.
• Dummy Trap and its detection.

Suggested Books: 

1. Gujrati, Basic Econometrics , 3rd Edition, ” McGraw Hill, 1993
2. Intrilligator Econometric Model, Techniques and Applications, N.Printice Hall, 1978 .
3. Johnston, Econometric Models,  McGraw Hill, 1984
4. Koutsoyiannis, Theory of Econometrics,  McMillan, 1978
5. Maddala, S. Econometrics, McGraw Hill, New York, 1978
6. Wonnacot, J. Econometrics,  John Wiley, New York & Wonnacot, E.
7. Madnani, M.K Introduction to  Econometrics Principles and Applications, (Latest Edition)
8. Pindyck & Econometric Models & Economic Forecasts, 3rd Rubenfeld Edition, McGraw Hill Inc.
9. Maddala, J. & Kim Unit  Roots,  Co-integration and Structural Change, Cambridge University Press, 1998.
10. Griffiths, Judge, The  Theory and  Practice of  Econometrics,   John Willey and Sons, Latest edition.

Research Articles:

1. Abadir (2002) Notation in Econometrics: a proposal for a standard. Econometric Journal, Vol. 5, issue 1, pages 76-96.
2. Abadir and  Jan    Magnum  (1993)  OLS  Bias in a  Nonstationary  Autogregression, Econometric Theory, Vol. 9, issue 1, pages 81-93.
3. Don Andrews (2003). A Bias-Reduced Log-Periodogram Regression Estimator for the Long Run, Memory Parameter,   Econometrica, 71, (2), 675-712.
4. Gunnar, Bardsen, (1999), Economic theory and econometric dynamics in modeling wages and prices in the United Kingdom,   Empirical Economics, 24, (3), 483-507.
5. Joshua, Angrist, (2001), Estimation of Limited Dependent Variable Models with  Dummy Endogenous Regressors: Simple Strategies for Empirical practice. Journal of Business and Economic Statistics, 19, (1), 2-16.
6. Luc, Bauwens, (1994),  Estimating End Use Demand:  A Bayesian Approach.  Journal of Business and Economic Statistics, 12, (2), 221-31.
7. Manuel, Arellano, (1992), Female Labour Supply and On-the-Job Search: An  Empirical Model Estimated Using Complementary Data Sets. Review of Economic Studies, 59, (3), 537-59.
8. Stephen, Roy, Bonal, (1991), Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equation. Review of Economic Studies, 58 (2), 277-97.