Week 1: Definition and scope of econometrics. Methodology of econometric research. Types of data. Difference between correlation, causation and regression. Population regression function and Sample regression function. The method of Ordinary Least Squares. Coefficient of determination.
Week 2: Significance of the error term. Assumption of OLS, Testing the parameters. Simple Linear Regression Model: estimation of model by method of ordinary least squares Properties of estimators, Goodness of fit, tests of hypotheses, Confidence intervals, Gauss- Markov Theorem.
Week 3: Multiple regression: Assumptions, Estimation of parameters, Properties of OLS estimators, Goodness of fit- R2 and adjusted -R2. Extension of linear regression model to nonlinear relationship.
Week 4: Analysis of variance as a statistical method, Regression Analysis and ANOVA, Testing the improvement of fit from additional variables, Chow test, Test of stability of regression coefficients to sample size.
Week 5: Problems of Heteroscedasticity- Detection, consequences, and remedies. Problem of Auto correlation - Detection, consequences, and remedies.
Week 6: Problem of Multicollinearity – detection, consequences, and remedies Specification error: Omission of relevant variables, inclusion of irrelevant variables, tests of specification error.
Week 7: Lags econometric models – Concepts, Almon’s approach to distributed lag model, Koyck model, Nerlove’s partial adjustment and Cagan’s adaptive expectation model.
Week 8: Dummy variables and it’s uses- Dummy variable as an alternative to Chow test, the interaction effects, use of Dummy variable in seasonal analysis. Proxy variables-concepts and uses. Linear Probability Model, Logistic regression.
Week 9: Simultaneous equation models, Introduction and examples, The simultaneous equation bias and inconsistency of OLS estimators, The identification problem, Rules of identification-order and rank conditions.
Week 10: Methods of estimating simultaneous equation system: Indirect least squares, Two stage least squares, Three stage least squares.
Week 11: Introduction to timeseries econometrics, Stationarity, Augmented Dickey Fuller tests, Cointegration, Error Correction Model, Autoregressive Distributed lag model approach to cointegration.
Week 12: Vector Autoregression, Granger Causality, Toda and Yamamoto test, Impulse Response Function, Variance Decomposition. An illustrative application of concepts learned using software.
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