Structural econometrics – methods and applications (CREST)


Objectif

The aim of this course is to provide an accessible introduction to structural estimation methods, with some illustrative examples and hands-on exercises. These methods have been used in labour economics but also in many other fields such as industrial organization, health economics, development economics. They allow researchers to confront rich theoretical models to the data in order to test their credibility and to carry out simulations of counterfactual policies. Modelling and estimation methods often need to be designed jointly to afford both identification and computational feasibility. We will aim to understand, with the aid of examples from the literature, what key features of the data, and what theoretical assumptions drive the values of parameter estimates and whether the conclusions drawn from the estimation are robust to alternative assumptions. We will also compare the relative appeals of structural estimation and reduced form estimation with natural experiments.

 

Plan

1.    Methods 1: Method of (Simulated) Moments, Method of Simulated Likelihood.
2.    Methods 2: Indirect Inference, Identification. Comparison with natural experiments.
3.    Applications 1: Hands-on structural estimation of simple examples.
4.    Applications 2: Three examples in labour research: Keane and Wolpin (2001), Rust (1987), Meghir, Narita, Robin (2015).

Références

References:

Adda, J., Cooper, R., & Cooper, R. W. (2003). Dynamic economics: quantitative methods and applications. MIT press.
Arcidiacono, P., & Jones, J. B. (2003). Finite mixture distributions, sequential likelihood and the EM algorithm. Econometrica, 71(3), 933-946.
Davidson, R., & MacKinnon, J. G. (2004). Econometric theory and methods (Vol. 5). New York: Oxford University Press.
French, E., & Taber, C. (2011). Identification of models of the labor market. In Handbook of Labor Economics (Vol. 4, pp. 537-617). Elsevier.
Keane, M. P. (2010). Structural vs. atheoretic approaches to econometrics. Journal of Econometrics, 156(1), 3-20.
Keane, M. P., Todd, P. E., & Wolpin, K. I. (2011). The structural estimation of behavioral models: Discrete choice dynamic programming methods and applications. In Handbook of labor economics (Vol. 4, pp. 331-461). Elsevier.
Keane, M. P., & Wolpin, K. I. (2001). The effect of parental transfers and borrowing constraints on educational attainment. International Economic Review, 42(4), 1051-1103.
McFadden, D. (1989). A method of simulated moments for estimation of discrete response models without numerical integration. Econometrica, 995-1026.
Meghir, C., Narita, R., & Robin, J. M. (2015). Wages and informality in developing countries. American Economic Review, 105(4), 1509-46.
Rust, J. (1987). Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher. Econometrica, 999-1033.
Rust, J. (1996). Numerical dynamic programming in economics. Handbook of computational economics, 1, 619-729.