|Course Type||Course Code||No. Of Credits|
Semester and Year Offered: 4th Semester
Course Coordinator and Team: Krishna Ram
Email of course coordinator: krishna[at]aud[dot]ac[dot]in
Prerequisites: Cross Section and Panel Data Econometrics –I (SLS2EC225)
This course is the second of a two-course sequence and deals with the more specialized methods in this area. The topics covered are: Advanced Panel Data Models, Panel Logit & Probit Models, Censored data, Sample Selection and Attrition, Measurement Error Models, Treatment Evaluation, and Missing data and Imputation techniques. The students would be introduced to the recent developments in these areas and would get a further opportunity to sharpen their empirical analysis skills.
On successful completion of this course students will be able to:
- 1. Test various economic theories using advanced panel data models, panel logit and panel probit models
- 2. Describe the problems relating with censored data, sample selection and attrition.
- 3. Identify the problem of measurement errors in case of both linear and non-linear regression models.
- 4. Compare and contrast between stratified and cluster sampling, and its consequences on regression modelling.
- 5. Outline the problems of missing data, and various imputation methods.
- 6. Evaluate the impact of policy intervention on outcome of interest.
- 7. Complete empirical project in areas as diverse as growth economics, macroeconomics, and development economics.
Brief description of modules/ Main modules:
- Advanced Panel Data Models: GMM estimation of Linear Panel Models, Dynamic Models, Nonlinear panel data Models,
- Panel Logit and Probit model: Estimation & Inferences
- Censored Data, Sample Selection, and Attrition: Data Censoring, Sample Selection, Truncated Regression, Sample selection and Attrition in Linear Panel data models
- Stratified Sampling and Cluster Sampling: Stratified Sampling, Cluster Sampling, Complex Survey Sampling
- Treatment Evaluation: Introduction, Treatment Effect and Selection bias, Difference and Difference Estimators, Regression discontinuity Design, Instrument Variables method.
- Measurement Error Models: Introduction, Measurement Error in Linear and Nonlinear Regression Models
- Missing Data and Imputation: Introduction, Missing Data Assumptions, Handling Missing Data, Regression Based Imputation, Multiple Imputation.
Assessment Details with weights:
- Two in class- exams, (30% weightage each)
- 1st class test- Mid-February
- 2nd class test- Mid - March
- Time Allowed: 3 hours. All question are compulsory. Simple non-programmable calculators are allowed.
- Empirical Project (40 % weightage)
- Each student has to present a topic of their project by 4th week of February which is worth 10% of overall weightage. The project is submitted in 2nd week of April which is worth 30% of overall weightage.
- Cameron, A.C. and Trivedi, P.K. Microeconometrics: Methods and Applications, Cambridge University Press, 2005.
- Wooldridge, J. Econometric Analysis of Cross Section and Panel Data, 2nd ed., MIT Press, 2010.
- Hsiao, C. Analysis of Panel Data, 2nd ed., Cambridge University Press, 2003
- Angrist, J.D and Pischke, J.-S.. Mostly Harmless Econometrics, Princeton University Press, 2008.
- Cameron, A.C. and Trivedi, P.K. Microeconometrics using Stata, 2nd ed., Stata Press, 2010
- Kennedy, P. A Guide to Econometrics, 6th ed., Wiley-Blackwell, 2008.
- Kleiber, C. and Zeilis, A. Applied Econometrics with R, Springer, 2008.
- Pearl, J.. Causality: Models, Reasoning and Inference, 2nd ed., Cambridge University Press, 2009.