ECON562: Empirical Microeconomics

Matthieu Chemin
Department of Economics
McGill University

Winter 2016


With limited public resources, determining which programs, reforms, policies are beneficial, and at what cost, is crucial, and allows public policy to be driven by evidence. However, evaluating programs is made difficult by the “counterfactual problem”: one cannot observe the outcomes or behavior of a participant, had (s)he not participated. This course will describe the standard OLS model, its limitations, and an improvement (panel data models). This course will then present the state-of-the-art empirical techniques used by economists to address the counterfactual issue (randomized experiments, instrumental variables, difference-in-differences, regression discontinuity design, selection models, matching).

For each of these approaches, we will give the basic intuition, discuss the necessary assumptions, present the strengths and weaknesses, analyze applications drawn from the literature. Moreover, each technique will be implemented by the participants in hands-on Stata sessions.



The main manual is Greene (2000) Econometric Analysis (4th Edition). Alternative options are Wooldridge (2000) Econometric Analysis of Cross Section and Panel Data and Davidson, MacKinnon (1993) Estimation and Inference in Econometrics. Research articles discussed in the course, exercises, and databases, will be available on this website.








Theoretical and practical lectures: TBD


Course content


Lecture 1: The standard ordinary least squares (OLS) model

Greene, chapter 6, "The classical multiple linear regression model: specification and estimation"
Greene, chapter 9, "Large-sample results and alternative estimators for the classical regression model"
Exercise 1


Lecture 2: The limitations of OLS

Greene, chapter 12, "Heteroscedasticity"
Greene, chapter 13, "Autocorrelated disturbances"
Greene, chapter 16, "Simultaneous-equations models"

Lecture 3: Panel data models

Wooldridge, chapter 10
Donohue, J. and Levitt, S. (2001), "The Impact of Legalized Abortion on Crime", Quarterly Journal of Economics, 116(2), 379-420.
Questionnaire 3

Lecture 4: Program evaluation

Wooldridge, chapter 18
Ravallion, M (2001), "The Mystery of the Vanishing Benefits: An Introduction to Impact Evaluation", World Bank Economic Review, 15(1), 115-40.
Blundell, R and Costa Dias, M (2000), "Evaluation Methods for Non-Experimental Data", Fiscal Studies, 21(4), 427-68.
Angrist, J. and Krueger, A. (1999), "Empirical strategies in Labor Economics", in Ashenfelter,O. and Card, D. Handbook of Labor Economics Volume III.

Paper to comment:
Abadie, A. and J. Gardeazabal (2003), "The Economic Costs of Conflict: a Case-Control Study for the Basque Country", American Economic Review 93(1): 113-132.
Questionnaire 4

Lecture 5: Randomized experiments

Stock, James and Mark Watson, "Introduction to Econometrics", chapter 11.
Gertler, Paul and Simon Boyce, "An Experiment in Incentive-based Welfare: The Impact of PROGRESA on Health in Mexico", 2001.
Miguel, Edward and Michael Kremer, "Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities", Econometrica, Vol. 72, No. 1 (January, 2004), 159–217.
Questionnaire 5
Exercise 2

Mid-term exam (Tuesday October 18)
Mid-term exam 2010
Final exam 2010

Lecture 6: Instrumental Variables

Wooldridge, chapter 5.
Wooldridge (introductory Econometrics), chapter 15.
Angrist, J. and A. Kruger (1991), "Does Compulsory School Attendance Affect Schooling and Earnings?",  The Quarterly Journal of Economics, 106(4), 979-1014.
Bound, Jaeger, and Baker (1995),  "Problems with Instrumental Variables Estimation When the Correlation Between the Instruments and the Endogeneous Explanatory Variable is Weak", Journal of the American Statistical Association, Vol. 90, No. 430. (Jun., 1995), pp. 443-450.
Questionnaire 6
Exercise 3
Data Cigarette

Lecture 7: Difference-in-Differences

Bertrand, M., Duflo, E. and S. Mullainathan (2004), "How Much Should We Trust Differences-In-Differences Estimates?", The Quarterly Journal of Economics, V.119, N.1, 1 February 2004, pp. 249-275(27).
Besley Timothy and Anne Case (2000),  "Unnatural Experiments? Estimating the Incidence of Endogenous Policies", The Economic Journal, Vol. 110, No. 467, Features. (Nov., 2000), pp. F672-F694.
Blundell, R. and M. Costa Dias (2000), "Evaluation Methods for Non_Experimental Data", Fiscal Studies, V.21, N.4, pp. 427-468.
Duflo, E. (2001), "Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment", American Economic Review, 91(4), 795-813.
Meyer, B., Viscusi, K., and D. Durbin (1995), "Workers' Compensation and Injury Duration: Evidence from a Natural Experiment", American Economic Review, 85(3), 322-340.
Questionnaire 7
Exercice 4


Lecture 8: Regression Discontinuity Design

Angrist, J. and V. Lavy (1999), "Using Maimonides' Rule to Estimate the Effect of Class Size On Scholastic Achievement", Quarterly Journal of Economics, V. 114, N. 2, 1 May 1999, pp. 533-575(43).
Clark, D. (2005), "Politics, Markets and Schools: Quasi-Experimental Evidence on the Impact of Autonomy and Competition from a Truly Revolutionary UK Reform".
Hahn, J., Todd, P. and W. Van Der Klaaw (2001), "Identification and Estimation of Treatment Effects with a Regression Discontinuity Design", Econometrica, V.69, N.1, January 2001, 201-209.
Pitt, M. and S. Khandker (1998), "The Impact of Group-Based Credit Programs on Poor Households in Bangladesh: Does the Gender of Participants Matter?", The Journal of Political Economy, 106(5), 958-996.
Questionnaire 8
Exercise 5

Lecture 9: Selection Models

Wooldridge, chapter 17
Rosen, S. and R. Willis (1979), "Education and Self-Selection", The Journal of Political Economy, 87(5), Part 2: Education and Income Distribution S7-S36.
Questionnaire 9

Lecture 10: Matching

Blundell, R. and M. Costa Dias (2000), "Evaluation Methods for Non_Experimental Data", Fiscal Studies, V.21, N.4, pp. 427-468.
Heckman, J., Ichimura, H. and P. Todd (1997), "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme", The Review of Economic Studies, 64(4), Special Issue: Evaluation of Training and Other Social Programmes, 605-654.
Sianesi, Barbara (2001), "Implementing propensity Score Matching Estimators with Stata".
Exercise 6


Lecture 11: Bootstrap

Efron, B. and R. Tibshirani (1986), "Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy", Statistical Science, V.1, N.1, February 1986, 54-75.
Eakin, B., McMillen, D., and M. Buono (1990), "Constructing Confidence Intervals Using the Bootstrap: An Application to a Multi-Product Cost Function", The Review of Economics and Statistics, V.72, N.2, May 1990, 339-344.



Tentative schedule:


  • Jan 7: introduction, lecture 1
  • Jan 14: lecture 1, 2
  • Jan 21: lecture 2, exercise 1
  • Jan 28: lecture 2, 3
  • Feb 4: lecture 3, exercise 1
  • Feb 11: program evaluation
  • Feb 18: randomized experiments
  • Feb 25: exercise 2, instrumental variables
  • March 4: Spring break
  • March 11: mid-term exam, IV
  • March 18: exercise 3 (IV), difference-in-differences
  • March 25: diff-in-diff, exercise 4 (DID)
  • April 1: regression discontinuity design
  • April 8: exercise 5 (RDD), selection models