The course of econometrics is taught in four parts (1 to 4), one per instructor (5 sessions of 3 hours each).

Exercise classes will be given by Mohamed Badrane Mahjoub (3 sessions of 3 hours for each part of the course). Problem sets will in general be handed out by the instructor in advance. Students are asked to make written preparations. They will have to use computers on their own for some of the problems: they should then bring their codes and results to the class; no computer work will take place during the class itself.

Mohamed Badrane Mahjoub will hold office hours every other week (dates and place to be announced). Use this opportunity for questions and individual help.

In general, materials for the class (slides, readings, lecture notes) will be available on the master web page before the lecture.

Cameron A. & Trivedi P., Microeconometrics, methods and applications, Cambridge University Press, 2005.
Wooldridge J., Introductory Econometrics: A Modern Approach, South-Western College Publishing, 2000.
Wooldridge J., Econometric Analysis of Cross Section and Panel Data, MIT Press, 2002.

The course as a whole accounts for 12 ECTS. The detail is as follows:
• 15%: short exam on part 1 (during one of the class sessions)
• 30%: exam on parts 1 and 2 (Fall term exam session)
• 5%: exercise class grade (based on written preparations and oral participation during the first 6 exercise class sessions)
• 15%: short exam on part 3 (during one of the class sessions)
• 30%: exam on parts 3 and 4 (Spring term exam session)
• 5%: exercise class grade (based on written preparations and oral participation during the last 6 exercise class sessions)

For those failing to reach a mark of 10 out of 20 (10/20), a catch-up exam (“session de rattrapage”) will be held as an oral examination in September.

The teamwork in econometrics is part of the requirement for the PPD master students. It accounts for 3 ECTS.
It is the opportunity for you to apply econometric methods on economic questions of your choice, using real datasets.
link on teamwork instructions

Linear Econometrics: 1. Introduction and Simple linear regression model

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Slides for the introduction
Slides for the simple regression model

Papers presented in class:
Krueger A., "Experimental estimates of education production functions", Quarterly Journal of Economics, 114, 2, 497-532, 1999.

Additional references:
Angrist, J. and S. Pischke, "Mostly harmless econometrics: an Empiricist's companion", Princeton University Press, 2009.

Textbook chapters:
Wooldridge introductory: chapter 2.

Statistics, introduction to Stata, simple regression model

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Preliminary: Exercises on statistics
Exercise sheet # 1
Wages.xls for exercise 4

Linear Econometrics: 2. Multivariate regression model

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Slides

Papers presented in class:
Banerjee A., E. Duflo, R. Glennerster and C. Kinnan "The miracle of microfinance? Evidence from a randomized evaluation", unpublished, 2009.


Textbook chapters:
Wooldridge introductory: chapter 3; ch 6-2,6-3,7-2,7-3,7-4.

Multivariate models

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Exercise sheet # 2
Coleman paper for exercise 3
eggs.dta for exercise 2

Linear Econometrics: 3. Tests, asymptotics

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Slides

Papers presented in class:
Textbook chapters:
Wooldridge introductory: chapters 4 and 5.

Tests

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Exercise sheet # 3
Jacoby paper for exercise 3
wage2.dta for exercise 4

Linear Econometrics: 4. Instrumental variables

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Slides

Papers presented in class:
Duflo E., "Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment", The American Economic Review, 91, 4, 795-813, 2001.
Angrist J. and A. Krueger, "Does Compulsory School Attendance Affect Schooling and Earnings?", The Quarterly Journal of Economics, 106, 4, 979-1014, 1991.

Textbook chapters:
Wooldridge advanced: sections 5.1-5.2.

Linear Econometrics: 5. Data application: What is crucial? Father's or mother's education?

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No paper presented in class


Do file in Stata

Data Demo_2.dta

Data educ_2.dta

Data parent.dta

Log file

Econometrics of evaluation: 1. Rubin's causal model, controled experiments

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Slides

Papers presented in class:
Krueger A., "Experimental estimates of education production function", Quarterly Journal of Economics, 114, 2, 497-532, 1999.

Additional references:
Duflo E., Glennerster R. & Kremer M., "Using randomization in development economics research: a toolkit", NBER Technical working paper 333, 2006.

Textbook chapters:
Cameron & Trivedi: sections 25.1-25.3.
Wooldridge: sections 18.1-18.2.

IV and Random experiments

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Exercise sheet # 4
wage2.dta for exercise

Econometrics of evaluation: 2. Advanced instrumental variables: weak instruments and heterogeneous effects

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Slides

Papers presented in class:
Angrist J. & Evans W., "Children and their parents' labor supply: Evidence from exogenous variation in family size", American Economic Review, vol.88, 450-477, 1998.
Imbens G. & Angrist J.,"Identification and estimation of local average treatment effects" Econometrica, vol.62, 467-475, 1994.
Bound J., Jaeger D. & Baker R., "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, 443-450, 1995.
Rosenzweig M. & Wolpin K.,"Natural 'Natural experiments' in economics", Journal of Economic Literature, vol.38, 827-874, 2000.

Additional references:
Angrist J. & Imbens G., "Two-stage least squares estimation of average causal effects in models with variable treatment intensity", Journal of the American Statistical Association, vol.90, 431-442, 1995.
Card D.,"Estimating returns to schooling: progress on some persistent econometric problems" Econometrica, vol.69, 1127-1160, 2001.
Cruz L. & Moreira M., "On the validity of econometric techniques with weak instruments", Journal of Human Ressources, vol.40, 394-410, 2005.

Textbook chapters:
Cameron & Trivedi: sections 4.8, 4.9 & 25.7.
Wooldridge: sections 18.4.

Experiments and advanced instrumental variables

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Exercise sheet # 5
Paper for exercise: Miguel E. & Kremer M., "Worms: identifying impacts on education and health in the presence of treatment externalities", Econometrica, 72, 1, 159-217, 2004.
Data for exercice:power.dta

Policy Evaluation: 3. Difference-in-difference and regression discontinuity design

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Slides diff-in-diff
Slides regression discontinuity

Papers presented in class:
Duflo E., "Schooling and labor market consequences of school construction in Indonesia: Evidence from an unusual policy experiment", American Economic Review, vol.91, n.4, September 2001.
Eissa N.& Liebman J., "Labor supply response to the earned income tax credit", Quarterly Journal of Economics, vol.111, n.2, May 1996.
Angrist J. & Lavy V.,"Using Maimonide's rule to estimate the effect of class size on scholastic achievement", Quarterly Journal of Economics, vol.114, 533-574, 1999.
Oreopoulos Ph., "Estimating average and local average treatment effects of education when compulsory schooling laws really matter", American Economic Review, vol.96, n.1, 2006.

Additional references:
Abadie A., "Semiparametric difference-in-differences estimators", Review of Economic Studies, vol. 72, 2005.
Athey S. & Imbens G., "Identification and inference in nonlinear difference-in-difference models", Econometrica, vol.74, March, 2006.
Bertrand M., Duflo E. & Mullainathan S., "How much should we trust differences-in-differences estimates", Quarterly Journal of Economics, vol.119, n.1, February 2004.
Conley T. & Taber Ch., "Inference with difference in differences with a small number of policy changes", NBER technical working paper 312, June 2005.
Hahn J., Todd P. & Van der Klaauw W., "Identification and estimation of treatment effects with a regression-discontinuity design", Econometrica, vol.69, n.1, January, 2001.
Imbens G. & Lemieux Th., "Regression discontinuity designs: A guide to practice", NBER Technical working paper 337, 2007.
Lee D. & Lemieux Th., "Regression discontinuity designs in economics", NBER Working paper 14723, 2009.

Textbook chapters:
Cameron & Trivedi: sections 25.5, 25.6.

Weak instruments and Regression discontinuity design

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Exercise sheet #6
Paper for exercice 2: Th. Lemieux and K. Milligan, ''Incentive effects of social assitance: A regression discontinuity approach'', Journal of Econometrics? 142, 2008
Data for exercice 1: ssex.dta

Policy Evaluation: 4. Matching estimators

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Slides

Papers presented in class:
Dehejia R. & et Wahba S., "Propensity score matching methods for nonexperimental causal studies", Review of Economics and Statistics, 84, 151-161, 2002.
Arceneaux K., Gerber A. & Green D., "Comparing experimental and matching methods using a large-scale field experiment on voter mobilization", Political Analysis, vol.14, n.1, 37-62, 2006.


Additional references:
Abadie A. & Imbens G., "On the failure of the bootstrap for matching estimators", Econometrica, 76, 1537-1558, 2008.
Caliendo M. & Kopeinig S., "Some practical guidance for the implementation of propensity score matching", IZA Discussion paper, 1588, 2005.
Frolich M., "Finite-sample properties of propensity-score matching and weighting estimators", Review of Economics and Statistics, 86, 77-90, 2004.
Heckman J., Ichimura H. & Todd, "Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme", Review of Economic Studies, vol. 64, 605-654, 1997.
Heckman J., Ichimura H. & Todd, "Matching as an econometric evaluation estimator", Review of Economic Studies, vol. 65, 261-294, 1998.
Guido Imbens: "Semiparametric Estimation of Average Treatment Effects under Exogeneity: A Review", Review of Economics and Statistics, 86, 4-29, 2004.
Lalonde R., "Evaluating the econometric evaluations of training programs with experimental data", American Economic Review, vol. 76, n. 4, 604-620, 1986.
Zhao Z., "Using matching to estimate treatment effects: data requirement, matching metrics and Monte Carlo evidence", Review of Economics and Statistics, 86, 91-107, 2004.
Zhao Z., "Sensitivity of propensity score methods to the specifications",IZA Discussion paper, 1873, 2005.

Textbook chapters:
Cameron & Trivedi: sections 25.4 & 25.8.
Wooldridge: sections 18.3.

Stata codes on Guido Imbens' website

Policy Evaluation: 5. Data application - Implementing a random experiment

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No Slides

No papers presented in class

Models for binary outcomes

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Slides

Lecture notes

Textbook chapters:
Cameron & Trivedi: sections 14.1 to 14.4

Binary outcomes

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Exercise sheet #7

Data for exercise 2
Paper for exercice 6: Angrist & Evans (AER98)
Data for exercice 6: ssex.dta

Models for binary outcomes

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Slides

Lecture notes

Textbook chapters:
Cameron & Trivedi: sections 15.1 to 15.4

Corner solutions, censored data and selected sample

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Slides

Lecture notes

Textbook chapters:
Cameron & Trivedi: sections 16.1 to 16.3

Discrete outcomes

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Exercise sheet #8

Data for exercise 2
Paper for exercise 3: Schmidt & Strauss (IER75)

Panel data

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Slides

Lecture notes

Textbook chapters:
Cameron & Trivedi: chapter 21

Corner solutions, censored data, selected samples and panel data

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Exercise sheet #9

Data for exercise 1 and 2
Paper for exercise 3: AJRY (AER 2005)
Paper for exercise 4: CK (AER 1994)

Data application: sample selectivity bias in a job search experiment

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No slides

ARIMA Processes

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Slides

Textbook chapters:
Hamilton: chapters 2, 3, 4, 17

ARIMA Processes

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Exercise sheet #10

Data for exercise 4
Program for exercise 4