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Marno Verbeek (EUR) Rotterdam School of Management and Econometric Institute Erasmus University Rotterdam P.O. Box 1738 3000 DR Rotterdam M.Verbeek@fbk.eur.nl 010-4082790
In many areas of economics the use of panel data techniques has become a standard tool. This course gives an introduction to the econometrics of panel data, with particular emphasis on the potential advantages of panel data as well as the additional complexities that may arise. The following topics will be covered: the pros and cons of panel data, linear models with random or fixed effects, dynamic models and GMM estimation, models with limited dependent variables and sample selection, incomplete panels and attrition. Throughout, several empirical examples will be discussed.
The structure of the course closely follows Chapter 10 of
A copy-edited pdf file of this chapter can be downloaded here. (Please do not circulate.) The bibliography of the entire book is provided here.
The latter two books are recommendable for economists.
The exam is due on 4 June, 2004 (strict!). A pdf version can be found here. Exercise 10.4 requires the use of empirical data, which are given in female.dat, with explanations in female.txt. Good luck!
Questions, comments, suggestions, ..., at M.Verbeek@fbk.eur.nl.
Topics in Econometrics of Nonlinear Panel Data Models
CIDE, Bertinoro, June 2003
Michael Lechner (SIAW, University of St. Gallen)
The course is designed to discuss nonlinear econometric methods for panel data at the level of a PhD course in economics. It consists of four lectures and one hands-on session in the PC lab, 3 hours each. Skills in econometrics at the level of Greene (2000), Econometric Analysis, are required.
TOPICS AND READING LIST1
Bold letters denote texts that should be read before the course. The other papers and books are useful background information.
1. Introduction
1.1 Panel data, random effects, fixed effects, linear and nonlinear models
Greene, William H. (2000), Econometric Analysis, 4th ed., London: Prentice-Hall, chapters 14.
Arellano, Manuel, and Bo Honoré (2001): "Panel Data Models: Some Recent Developments", in J.J. Heckman and E. Leamer, Handbook of Econometrics, Vol. V., ch. 53, Amsterdam: North-Holland, 3229-3296.
1.2 Nonlinear models for cross-sectional data: A brief overview
Greene, William H. (2000), Econometric Analysis, 4th ed., London: Prentice-Hall, chapters 19, 20.
1.3 Estimation methods
Newey, W.K. and D. McFadden (1994): "Large Sample Estimation and Hypothesis Testing", in Engle, R.F. and D.L. McFadden, Hrsg., Handbook of Econometrics, Vol. 4, 2113-2245, Amsterdam: North-Holland.
1
A reference list with more background material will be provided during the course.
0.1 18.04.03
1. 1.3.1 Maximum Likelihood
2. 1.3.2 GMM and GMM with conditional moment restrictions
3. 1.3.3 Simulation methods
Börsch-Supan, A. and Hajivassiliou, V.A. (1993): "Smooth Unbiased Multivariate Probabilities Simulators for Maximum Likelihood Estimation of Limited Dependent Variable Models", Journal of Econometrics, 58, 347-368.
Hajivassiliou, Vassilis, and Daniel L. McFadden (1998): "The Method of Simulated Scores for the Estimation of LDV Models", Econometrica, 66, 863-896.
Hajivassiliou, Vassilis, Daniel McFadden, and Paul Ruud (1996): "Simulation of multivariate normal rectangle probabilities and their derivatives: Theoretical and computational results", Journal of Econometrics, 72, 85-134.
Lerman, S. and Manski, C.F. (1981): "On the Use of Simulated Frequencies to Approximate Choice Probabilities", in Manski, C.F. and McFadden, D. (eds.), Structural Analysis of Discrete Data with Econometric Applications, Cambridge: MIT-Press, 305-319.
McFadden, D. (1989): "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration", Econometrica, 57, 995-1026.
2 Random effects with strict exogeneity
2.1 Maximum likelihood estimation
Butler, I.S. and Moffitt, R. (1982): "A Computationally Efficient Quadrature Procedure for the One-Factor Multinomial Probit Model", Econometrica, 50, 761-764.
Guilkey, D.K. and Murphy, J.L. (1993): "Estimation and Testing in the Random Effects Probit Model", Journal of Econometrics, 59, 301-317.
Lee, Lung-fei (2000): "A numerically stable quadrature procedure for the one-factor random component discrete choice model", Journal of Econometrics, 95, 117-129.
2.2 Simulated Maximum Likelihood
Gourieroux, C. and Monfort, A. (1993a): "Simulation-based Inference: A Survey with Special Reference to Panel Data Models", Journal of Econometrics, 59, 5-33.
Keane, M.P. (1994): "A Computationally Practical Simulation Estimator for Panel Data", Econometrica, 62, 95-116.
Inkmann, Joachim (2000): "Misspecified heteroscedasticity in the panel probit model: A small sample comparison of GMM and SML estimators", Journal of Econometrics, 97, 227-259..
2.3 GMM based on marginal moments
Avery, R., Hansen, L. and Hotz, V. (1983): "Multiperiod Probit Models and Orthogonality Condition Estimation", International Economic Review, 24, 21-35.
Bertschek, Irene, and Michael Lechner (1998): "Convenient Estimators for the Panel Probit Model", Journal of Econometrics, 87, 329-371.
Lechner, Michael and Jörg Breitung (1996): "Some GMM Estimation Methods and Specification Tests for Nonlinear Models", in Laslo Matyas, Patrick Sevestre, eds., The Econometrics of Panel Data, Vol. 2, 2nd ed., Kluwer, 583-612.
0.2 18.04.03
Inkmann, Joachim (2000): "Misspecified heteroscedasticity in the panel probit model: A small sample comparison of GMM and SML estimators", Journal of Econometrics, 97, 227-259..
2.4 Empirical / Monte Carlo examples
3 Fixed effects with strict exogeneity
3.1 Introduction
Inconsistency of the random effects estimators for small T. Unfeasibility of the tricks that work in the linear model.
3.2 Fixed effects binary choice
Chamberlain, G. (1984): "Panel Data", in Griliches, Z. and Intriligator, M.D. (eds.), Handbook of Econometrics, Vol. II, Ch. 22, Amsterdam: North-Holland.
Chamberlain, G. (1980): "Analysis of Covariance with Qualitative Data", Review of Economic Studies, 47, 225-238.
Charlier, Erwin (1997): "Panel smoothed maximum score", Chapter 2 in Charlier, Erwin, Limited Dependent Variable Models for Panel Data, PhD-Thesis, Tilburg University.
Charlier, Erwin (1997): "Weighted smoothed maximum score", Chapter 7 in Charlier, Erwin, Limited Dependent Variable Models for Panel Data, PhD-Thesis, Tilburg University.
Chen, Songnian (2000): "Rank estimation of a location parameter in the binary choice model", Journal of Econometrics, 98, 317-334.
Honoré, Bo E., and Arthur Lewbel (2002): "Semiparametric Binary Choice Panel Data Models Without Strictly Exogenous Regressors", Econometrica, 70, 2053-2063.
Horowitz, J.L. (1992): "A Smoothed Maximum Score Estimator for the Binary Response Model", Econometrica, 60, 505-531.
Laisney, F., Lechner, M. and Pohlmeier, W. (1993): "Semi-Nonparametric Estimation of Binary Choice Models Using Panel Data", Recherches Économiques de Louvain, 58, 329-343.
Laisney, Francois, and Michael Lechner (2003): "Almost Consistent Estimation of Panel Probit Models with 'Small Fixed Effects'," forthcoming in Econometric Reviews.
Lee, Myong-jae (1999):"A root-N consistent semiparametric estimator for related-effect binary response panel data", Econometrica, 67, 427-433.
Magnac, Thierry (2001): "Binary variables and fixed effects: generalizing conditional logit", mimeo. important papers, shows that quasi-differencing is possible in many nonlinear models à Econometica ?
Manski, C.F. (1987): "Semiparametric Analysis of Random Effects Linear Models from Binary Panel Data", Econometrica, 55, 357-362.
3.3 Fixed effects count data model
Blundell, Richard, Rachel Griffith, and Frank Windmeijer (2002): "Individual effects and dynamics in count data models", Journal of Econometrics, 108, 113-131.
Montalvo, Jose G. (1997): "GMM Estimation of Count-Panel-Data Models With Fixed Effects and Predetermined Instruments", Journal of Business & Economic Statistics, 15, 82-89.
0.3 18.04.03
3.4 Censoring, truncation and selection
Honoré, Bo E. (1992): "Trimmed LAD and Least Squares Estimation of Truncated and Censored Regression Models with Fixed Effects", Econometrica, 60, 533-565.
Honoré, Bo E. (1993): "Orthogonality Conditions for Tobit Models with Fixed Effects and Lagged Dependent Variables", Journal of Econometrics, 59, 35-61.
Honoré, Bo E., and J.L. Powell (1994): "Pairwise Difference Estimators of Censored and Truncated Regression Models", Journal of Econometrics, 64, 241-278. Kyriazidou, Ekaterini (1997): "Estimation of a Panel Sample Selection Model", Econometrica, 65, 13351364.
Kyriazidou, Ekaterini (2001): "Estimation of Dynamic Panel Data Sample Selection Models", The Review of Economic Studies, 68, 543-572. Identification by exclusion restriction in selection equation and strictly exogenous regressors in outcome equation
Charlier, Erwin, Bertrand Melenberg, and Arthur van Soest (2000): "Estimation of a censored regression panel data model using conditional moment restrictions efficiently", Journal of Econometrics, 95, 25-56.
3.5 Empirical / Monte Carlo examples
4 Dynamics
1. 4.1 Random effects
2. 4.2 Fixed effects
Hahn, Jingyong (2001): "The Information Bound of a Dynamic Panel Logit Model with Fixed Effects", Econometric Theory, 17, 913-932. Carrasco, Raquel (1998): „Binary Choice with Binary Endogenous Regressors in Panel Data: Estimating the Effects of Fertility on Female Labour Participation“, JBES, 19, 385-394. Erdem, Tülin, and Baohong Sun (2001): "Testing for Choice Dynamics in Panel Data", JBES, 19,142-152.
Honoré, Bo, and Ekaterini Kyriazidou (2000): "Panel data discrete choice models with lagged dependent variables", Econometrica, 68, 839-874.
Charlier, Erwin (1997): "Efficient Estimation in a Censored Regression Panel Data Model, in Chapter 3 in Charlier, Erwin, Limited Dependent Variable Models for Panel Data, PhD-Thesis, Tilburg University.
Hu, Luojia (2002): "Estimation of a Censored Dynamic Panel Data Model", Econometrica, 70, 24992517.
Lechner, M. (1993b): "Estimation of Limited Dependent Variable Habit Persistence Models on Panel Data with an Application to the Dynamics of Self-employment in the Former East Germany", in Bunzel, H., Jensen, P. and Westergård-Nielson, N. (eds.), Panel Data and Labour Market Dynamics, Amsterdam: North-Holland, 263-283.
4.3 Empirical / Monte Carlo examples
0.4 18.04.03
5. Causal analysis
1. 5.1 Causality, potential outcomes, and identifying assumptions
2. 5.2 Panel data to control selection bias in the static causal model
Eissa, Nada (1996) “Labor Supply and the Economic Recovery Tax Act of 1981.” In Martin Feldstein and James Poterba, eds., Empirical Foundations of Household Taxation. Chicag University of Chicago Press. 5-32.
Heckman, James (1996) “Comment.” In Martin Feldstein and James Poterba, eds., Empirical Foundations of Household Taxation. Chicag University of Chicago Press. 32-38.
Heckman, James and Richard Robb (1985) “Alternative Methods of Evaluating the Impact of Interventions.” In: James Heckman and Burton Singer, eds., Longitudinal Analysis of Labour Market Data. New York: Cambridge University Press. 156-245.
Heckman, James and V. Joseph Hotz (1989) “Choosing Among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training.” Journal of the American Statistical Association. 84: 862-880.
Heckman, James, Robert LaLonde and Jeffrey Smith [HLS] (1999) “The Economics and Econometrics of Active Labor Market Programs.” In Orley Ashenfelter and David Card, eds., Handbook of Labor Economics, Volume 3A. Amsterdam: North-Holland. 1865-2097; Section 7, especially Section 7.6.
Meyer, Bruce (1995) “Natural and Quasi-Experiments in Economics.” Journal of Business and Economic Statistics. 13: 151-161.
Moffitt, Robert (1991) “Program Evaluation with Nonexperimental Data.” Evaluation Review. 15(3). 291-314.
Combining Longitudinal and Matching Methods
Eichler, Martin and Michael Lechner (2002) “An Evaluation of Public Employment Programmes in the East German State of Sachsen-Anhalt.” Labour Economics.
Heckman, James , Hidehiko Ichimura, Petra Todd and Jeffrey Smith (1998) “Characterizing Selection Bias Using Experimental Data.” Econometrica. 66(5): 1017-1098.
Rosenbaum, Paul (2001) “Stability in the Absence of Treatment.” Journal of the American Statistical Association. 96. 210-219.
5.3 Dynamic causal models and panel data
Robins, James (1986) “A new approach to causal inference in mortality studies with sustained exposure periods - Application to control of the healthy worker survivor effect.” Mathematical Modelling, 7:1393-1512, with 1987 Errata to “A new approach to causal inference in mortality studies with sustained exposure periods - Application to control of the healthy worker survivor effect.”' Computers and Mathematics with Applications, 14:917-921; 1987 Addendum to “A new approach to causal inference in mortality studies with sustained exposure periods - Application to control of the healthy worker survivor effect.” Computers and Mathematics with Applications, 14:923-945; and 1987 Errata to “Addendum to 'A new approach to causal inference in mortality studies with sustained exposure periods - Application to control of the healthy worker survivor effect'.” Computers and Mathematics with Applications, 18:477.
Lechner, Michael and Ruth Miquel (2001) “A Potential Outcome Approach to Dynamic Programme Evaluation – Part I: Identification.” Discussion paper 2001-16, University of St. Gallen, under revision.
Lechner, Michael (2003): "Dynamic Matching Estimation," mimeo.
Sianesi, Barbara (2001) “An Evaluation of the Active Labour Market Programmes in Sweden.” IFAU discussion paper.
0.5 18.04.03
5.4 Causal interpretation of standard nonlinear panel estimators
Lechner, Michael (2002): "Eine Übersicht über gängige Modelle der Panelökonometrie und ihre kausale Interpretation," Allgemeines Statistisches Archiv, 86, 125-143, 2002.
5.5 Empirical / Monte Carlo Examples
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