This course unit aims at equipping students with in-depth regression analysis and related Techniques. The course will involve simple and multiple linear regressions (OLS); matrix representation of the regression model; statistical inferences (correlation analysis, T-test, F-test CHi2 test, ANOVA) for regression model; properties of OLS; diagnostics and remedies for multicollinearity and heteroskedasticity; diagnostics for model selection, variable selection, transformations (such as log, Box-Cox, etc.); appropriate Statistical packages (e.g. STATA) will be used in the course unit to demonstrate how to apply the Techniques on real data.
Model specification and data: developing a conceptual framework; types and sources of data, data mining, model specification and data generation; the mathematical Programming approach to policy analysis: the classical MP models, limitations of of MP models and extension to positive mathematical Programming, classification of mathematical Programming models commonly used in policy analysis, application in hypothesis testing, application in analyzing policy instruments and commodity policy, application in forecasting; econometric approach to policy analysis.
Classification of econometrics models, linear and none linear models, limited and censor-dependent variable approaches (logit, probit, tobit, and their extensions such as multinomial logit and probit etc.), system of equations or simultaneous equations, application in hypothesis testing, application in analyzing policy instruments and commodity policy, application in forecasting, the problem of causality in policy analysis, limitation of econometric models; impact assessment: propensity score matching, regression discontinuity designs, panel data to analyze with staggered entry.
Multivariate analysis· Introduction to Econometrics Preview
· Simple Linear Regression: Basic Concepts
· Simple Linear Regression: Estimation of Parameters
· Simple Linear Regression: Outliers
· Multiple Linear Regression: Basic Concepts
· Multiple Linear Regression: Estimation of Parameters
· Autocorrelation: Durbin-Watson Test Statistic
· Multivariate Regression
· Logistic Regression
· Polynomial Regression
· Autoregressive Model
· Vector Autoregressive Model
. Limited and censor-dependent variable approaches (logit, probit, tobit, and their extensions such as multinomial logit and probit etc.),
Detailed Course Content
|
Topic |
LH |
PH |
|
Simple and multiple linear regressions (OLS); matrix representation of the regression model; statistical inferences (correlation analysis, T-test, F-test CHi2 test, ANOVA) for regression model; |
4 |
|
|
Properties of OLS; diagnostics and remedies for multicollinearity and heteroskedasticity; diagnostics for model selection, variable selection, transformations (such as log, Box-Cox, etc.); |
4 |
18 |
|
Appropriate Statistical packages (e.g. STATA) will be used in the course unit to demonstrate how to apply the Techniques on real data. |
6 |
|
|
Model specification and data: developing a conceptual framework; types and sources of data, data mining, model specification and data generation; the mathematical Programming approach to policy analysis: |
4 |
|
|
Application in hypothesis testing, application in analyzing policy instruments and commodity policy, application in forecasting; econometric approach to policy analysis. |
4 |
|
|
Classification of econometrics models, linear and none linear models, limited and censor-dependent variable approaches (logit, probit, tobit, and their extensions such as multinomial logit and probit etc.), |
4 |
2 |
|
System of equations or simultaneous equations, application in hypothesis testing, application in analyzing policy instruments and commodity policy, |
8 |
|
|
Application in forecasting, the problem of causality in policy analysis, limitation of econometric models; |
4 |
|
|
Application in forecasting, the problem of causality in policy analysis, limitation of econometric models impact assessment |
4 |
|
|
propensity score matching, regression discontinuity designs, panel data to analyze with staggered entry |
4 |
|
|
Multivariate analysis and experimental economics (factor analysis, principal component analysis, cluster analysis, discriminant analysis), auctions theory. |
4 |
|
Referencences:
Econometrics (2022) by Bruce Hansen, recommended but not required.
https://www.ssc.wisc.edu/~bhansen/econometrics/
Other complementary Texts include:
Angrist, J.D. and J. Pischke (2009): Mostly Harmless Econometrics - An Empiricists Companion, Princeton University Press
Amemyia, T. (1985): Advanced Econometrics, Blackwell
Berndt, E.R., The Practice of Econometrics: Classic and Contemporary. Addison-Wesley, 1991.
Chow, G. C. Econometrics. McGraw Hill, 1983.
Cameron, A. C. and Travedi, P.K. (2005): Microeconometrics – Methods and Applications, Cambridge University Press
Davidson, R. and McKinnon, J.G. (1993): Estimation and Inference in Econometrics, Oxford University Press
Drhymes, P. (1994) Topics in Advanced Econometrics, Vol 2: Linear and Nonlinear Simultaneous Equations, Springer Verlag
Green, W.A., Econometric Analysis, 8th edition, Prentice Hall.
Goldberger, A. S., A Course in Econometrics. Harvard University Press, 1991.
Hayashi, F. (2000). Econometrics. Princeton: Princeton University Press
Malinvaud, E. Statistical Methods of Econometrics, 3rd Edition. North-Holland, 1980.
Ramanathan, R. Statistical Methods in Econometrics, Academic Press, 1993.
Ruud, P.A. (2002): An Introduction to Classical Econometric Theory, Oxford University Press.
Sargan, J. D. Lectures on Advanced Econometric Theory. Basil Blackwell, 1988
Wooldridge, J.M . (2002): Econometric Analysis of Cross-Section and Panel Data, MIT Press.
Schervish, M.J. (1995): Theory of Statistics, Springer Verlag
Spanos, A. Statistical Foundation of Econometric Modeling. Cambridge University Press, 1986.
Theil, H. (1971): Principles of Econometrics, Wiley
White, H. (2000): Asymptotic Theory for Econometricians (Revised Edition), Academic Press
Zellner, A. An Introduction to Bayesian Inference in Econometrics. Wiley-Interscience, 1996.
Gurmu, Shiferaw, and Pravin K. Trivedi,
(1996)"Excess Zeroes in Count Models for Recreational Trips." Journal
of Business and Economics Statistics 14: 469-477.
Blundell, Richard, Rachel Griffith, and Frank Windmeijer, (1984)"Individual Effects and Dynamics in Count Data Models," London: Institute for Fiscal Studies Working Paper No. W99/3 (1999).
Hausman, Jerry A., Zvi GriliLHes, and Bronwyn H. Hall, "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica 52: 909-938.
Hall, Bronwyn H., Zvi GriliLHes, and Jerry A. Hausman, (1986)"Patents and R&D: Is There a Lag?" International Economic Review 27: 265-283.
Montalvo, Jose Garcia, (1993) "GMM Estimation of Count Panel Data Models with Fixed Effects and Predetermined Instruments," Journal of Business and Economic Statistics 15: 82-89.
LHamberlain, Gary, "Multivariate Regression Models for Panel Data, (1982)" Journal of Econometrics 18:5-46.
Exercise Collections:
P. C. B. Phillips and M. R. Wickens, Exercises in Econometrics, Vol. I & II. Allen/Ballinger, 1978.
K. Abadir and J. Magnus, Econometric Exercises Vol 1: Matrix Algebra. Cambridge University Press, 2005.