This course provides a comprehensive introduction to basic econometric concepts and techniques. It covers estimation and diagnostic testing of simple, multiple regression models, panel data models, and dummy variable regression with qualitative response regression models.
At the end of the course, students should be able to:
Unit I (2 weeks)
Introduction to Econometrics and an overview of its applications; Simple Regression with Classical Assumptions; Least Square Estimation And BLUE, Properties of estimators, Multiple Regression Model and Hypothesis Testing Related to Parameters – Simple and Joint. Functional forms of regression models.
Gujarati, N. Damodar. Basic Econometrics. New Delhi: McGraw Hill. [ Chapter 1-9]
Gujarati, N. Damodar. Econometrics by Examples. New Delhi: McGraw Hill. [Chapter1-3]
Unit II (4 weeks)
Violations of Classical Assumptions: multicollinearity, heteroscedasticity, autocorrelation and model specification errors, their identification, their impact on parameters; tests related to parameters and impact on the reliability and the validity of inferences in case of violations of Assumptions; methods to take care of violations of assumptions, goodness of fit.
Time Series econometrics: stationary stochastic processes, nonstationary Stochastic Processes, unit root stochastic processes, trend Stationary and difference Stationary stochastic processes. Tests of stationarity- graphical analysis and autocorrelation function (ACF) and correlogram statistical significance of autocorrelation coefficients. The unit root test – the augmented dickey-fuller (ADF) test. Transforming nonstationary financial time series – difference stationary processes and trend- Stationary process
Gujarati, N. Damodar. Basic Econometrics. New Delhi: McGraw Hill. [Chapter 10-13 and 21-22]
Gujarati, N. Damodar. Econometrics by Examples. New Delhi: McGraw Hill. [Chapter 4-7 and 13]
Unit III (3 weeks)
Panel data regression models – the importance of panel data, Pooled OLS regression of charity function, the fixed effects least squares dummy variable (LSDV) model, Limitations of the fixed effects LSDV model, the fixed effect within group (WG) estimator, the random effects model (REM) or error components model (ECM), fixed effects model vs. random effects model and properties of various estimators. Stochastic regressors and the method of instrumental variables- the problem of endogeneity, the problem with stochastic regressors, reasons for correlation between regressors and the error term and the method of instrumental variables (2SLS).
Gujarati, N. Damodar. Basic Econometrics. New Delhi: McGraw Hill. [Chapter 16]
Gujarati, N. Damodar. Econometrics by Examples. New Delhi: McGraw Hill. [17 and 19]
Unit IV (3 weeks)
Dummy variables: Intercept dummy variables, slope dummy variables, Interactive dummy variables, Use of Dummy Variables to model qualitative/Binary/Structural changes, Other Functional Forms, Qualitative Response Regression Models or Regression Models with Limited Dependent Variables – Use of Logit, and Probit Models
Recommendation Computer Package to be Used: Use of software like E Views, R and STATA solving real life problems and checking assumptions and taking care of assumptions violations and testing goodness of fit, Panel data regression models. And used in Logit, and Probit Models.
Gujarati, N. Damodar. Basic Econometrics. New Delhi: McGraw Hill. [Chapter 9 and 15]
Gujarati, N. Damodar. Econometrics by Examples. New Delhi: McGraw Hill. [3 and 8]