Cross Section and Panel Data Econometrics -I

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Course TypeCourse CodeNo. Of Credits
Discipline ElectiveSLS2EC2254

Semester and Year Offered: 3rd Semester

Course Coordinator and Team: Krishna Ram

Email of course coordinator:

Prerequisites: Econometrics and Data Analysis (SLS2EC211)

Course Objectives/Description:

This course deals with the various econometrics techniques that we generally use when we deal with the cross sectional and panel data set. The course discusses some of the econometric challenges involved testing economic relationship in developing countries. The course uses both theory and empirical techniques to teach various econometric methods. The emphasis would be on imparting skills that enable students to carry out independent empirical work.

The course includes various study papers which specifically deal with econometrics issues at hand. The objectives are to understand the econometric problems and methods used to solved them.

Course Outcomes:

On successful completion of this course students will be able to:

  1. Explain how a panel data regression model is different from either a cross section or time series regression model.
  2. Identify various econometric problems associated with cross section and panel data regression models.
  3. Estimate & interpret linear panel data regression model using econometric software package, STATA.
  4. Estimate different type of categorical dependent variable regression models using econometric software, STATA
  5. Complete empirical project relating with cross-sectional and Panel data set using software package, STATA

Brief description of modules/ Main modules:

  1. Review of OLS, OLS as Maximum Likelihood Estimator, Endogeneity, Causality and the use of Randomized Control Trials (RCT’s).
  2. Basic linear panel data methods: Pooled OLS, Fixed and Random effects estimation.
  3. Binary dependant variables: Linear Probability, Logit and Probit models.
  4. Ordinal Outcomes: Ordered logit and ordered Probit models.
  5. Multinomial choice models: Multinomial Logit and Probit models.
  6. Limited Dependent Variables: The Tobit model

Assessment Details with weights:

1, Two in class- exams, (30% weightage each)

1st class test- 3rd week of September

2nd class test- 4th week of October

Time Allowed: 3 hours. All question are compulsory. Simple non-programmable calculators are allowed.

2. Empirical Project ( 40% weightage)

Each student has to present a synopsis of their empirical project by 1st week of October which is worth 10% of overall weightage. The project is submitted in 1st week of November which is worth 30% of overall weightage.

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.


  • Krueger, A. (1993), How Computers Have Changed the Wage Structure: Evidence from Micro Data, Quarterly Journal of Economics, 108, no. 1, 33-60.
  • Mauldin, P. W. and Berelson, B(1978), Condition of fertility decline in developing countries, 1965-75, Studies in Family Planning, 9,89-147.
  • J. J. Donahue III and Steven D. Levitt,(2001), The Impact of Legalised Abortion on Crime, Quarterly Journal of Economics, pp 389-420
  • Duflo, E. (2001), Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment, American Economic Review 91, no. 4, 795-813.
  • Gertler, Paul, and Simon Boyce. (2001), An Experiment in Incentive-Based Welfare: The Impact of PROGESA on Health in Mexico, Working Paper.
  • Ram, Krishna (2017), Explaining Calorie consumption Puzzle in India: An Empirical Study based on National and International Data-sets since 1990s, Social Scientist, Vol.45, No. 532-533, pg. 35-53

Some additions in list of articles mentioned above may be made later.