Econometrics Track 3: Microeconometric Methods & Applications

Home/ Econometrics Track 3: Microeconometric Methods & Applications
Course TypeCourse CodeNo. Of Credits
Foundation CoreNA2

Semester and Year Offered:I

Course Coordinator and Team: Krishna Ram

Email of course coordinator:krishna[at]aud[dot]ac[dot]in

Pre-requisites:Knowledge of graduate level econometrics is required.

Course Objectives/Description:

This course deals with the various micro econometrics techniques that we generally use when we deal with the cross sectional and panel data set. The course uses both theory and empirical techniques to teach various econometric methods. The objectives are to understand the econometric problems and methods used to solved them. The emphasis would be on imparting skills that enable students to carry out independent empirical work.

Brief description of modules/ Main modules:

  1. Review of OLS, Omitted Variable Problem, and Measurement Error
  2. Basic linear panel data methods: Pooled OLS, Fixed and Random effects estimation
  3. Regression models for categorical dependent variables; Estimation, interpretation, testing and measure of fit.
  4. Logit and Probit modes
  5. Ordinal Logit and Probit models
  6. Multinomial Logit and Probit models
  7. Tobit model

Assessment Details with weights:

  • Problem sets (3) - 20% each
  • Term Paper – 40%

Reading List:

  • Angrist, J.D and Pischke, J. S. (2008), Mostly Harmless Econometrics: An Empiricist’s Companion, Princeton University Press.
  • Goldberger, A. S. (1991). A course in econometrics. Harvard University Press.
  • Gujarati, D.N, Porter, D. C. &Gunasekar, S (2009), Basic Econometrics, 5th ed. Tata McGrow Hill, New Delhi.
  • Long, S. J (1997). Regression models for categorical dependent variables, Advanced Quantitative technique in social Sciences series, Vol 7, Sage Publications, London
  • Long, S. J. &Freese, J. (2006). Regression models for categorical dependent variables using Stata. Stata press.
  • Wooldridge, J. (2010), Econometric Analysis of Cross Section and Panel Data , 2nd ed., MIT Press.