Generalized Indirect Inference for Discrete Choice Models
This paper develops and implements a practical simulation-based method for estimating dynamic discrete choice models. The method, which can accommodate lagged dependent variables, serially correlated errors, unobserved variables, and many alternatives, builds on the ideas of indirect inference. In particular, the method uses an auxiliary model---typically a linear probability model---to summarize the statistical properties of the observed and simulated data. The method then chooses the structural parameters so that the coefficients of the auxiliary model in the simulated data match as closely as possible those in the observed data. The main difficulty in implementing indirect inference in discrete choice models is that the objective surface is a step function, rendering useless gradient-based optimization methods. To overcome this obstacle, this paper shows how to smooth the objective surface. The key idea is to use a smooth function of the latent utilities as the dependent variable in the auxiliary model. As the smoothing parameter goes to zero, this function delivers the discrete choice implied by the latent utilities, thereby guaranteeing consistency. A set of Monte Carlo experiments shows that the method is fast, robust, and nearly as efficient as maximum likelihood when the auxiliary model is sufficiently rich
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||11 Aug 2004|
|Date of revision:|
|Contact details of provider:|| Phone: 1 212 998 3820|
Fax: 1 212 995 4487
Web page: http://www.econometricsociety.org/pastmeetings.asp
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Thierry Magnac & Jean-Marc Robin & Michael Visser, 1995.
"Analysing incomplete individual employment histories using indirect inference,"
- Magnac, Thierry & Robin, Jean-Marc & Visser, Michael, 1995. "Analysing Incomplete Individual Employment Histories Using Indirect Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(S), pages S153-69, Suppl. De.
- Daniel McFadden, 1987.
"A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration,"
464, Massachusetts Institute of Technology (MIT), Department of Economics.
- McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
- Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
- Lopez Garcia, Italo, 2015. "Human Capital and Labor Informality in Chile: A Life-Cycle Approach," Working Papers 1087, RAND Corporation.
- Kristensen, Dennis & Shin, Yongseok, 2012.
"Estimation of dynamic models with nonparametric simulated maximum likelihood,"
Journal of Econometrics,
Elsevier, vol. 167(1), pages 76-94.
- Dennis Kristensen & Yongseok Shin, 2008. "Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood," CREATES Research Papers 2008-58, Department of Economics and Business Economics, Aarhus University.
- Lee, Lung-Fei, 1997.
"Simulated maximum likelihood estimation of dynamic discrete choice statistical models some Monte Carlo results,"
Journal of Econometrics,
Elsevier, vol. 82(1), pages 1-35.
- Lee, L.F., 1994. "Simulated Maximum Likelihood Estimation of Dynamic Discrete Choice Statistical Models--Some Monte Carlo Results," Papers 94-06, Michigan - Center for Research on Economic & Social Theory.
- Fermanian, Jean-David & Salani , Bernard, 2004. "A Nonparametric Simulated Maximum Likelihood Estimation Method," Econometric Theory, Cambridge University Press, vol. 20(04), pages 701-734, August.
- Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-31, May.
- Tong Li & Bingyu Zhang, 2015. "Affiliation and Entry in First-Price Auctions with Heterogeneous Bidders: An Analysis of Merger Effects," American Economic Journal: Microeconomics, American Economic Association, vol. 7(2), pages 188-214, May.
- Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
- Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
When requesting a correction, please mention this item's handle: RePEc:ecm:nawm04:512. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)
If references are entirely missing, you can add them using this form.