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. The main difficulty in implementing indirect inference in discrete choice models is that the objective surface is a step function, rendering gradientbased optimization methods useless. To overcome this obstacle, this paper shows how to smooth the objective surface. The key idea is to use a smoothed 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. We establish conditions on the smoothing such that our estimator enjoys the same limiting distribution as the indirect inference estimator, while at the same time ensuring that the smoothing facilitates the convergence of gradient-based optimization methods. 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.
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- 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.
- Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
- 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.
- Meghan M. Skira, 2015. "Dynamic Wage And Employment Effects Of Elder Parent Care," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 63-93, February.
- Meghan Skira, 2012. "Dynamic Wage and Employment Effects of Elder Parent Care," Boston College Working Papers in Economics 792, Boston College Department of Economics, revised 16 Aug 2013.
- Meghan Skira, 2013. "Dynamic Wage and Employment Effects of Elder Parent Care," 2013 Meeting Papers 79, Society for Economic Dynamics.
- 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.
- Daniel McFadden, 1987. "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Working papers 464, Massachusetts Institute of Technology (MIT), Department of Economics.
- Cristina Lopez-Mayan, 2014. "Microeconometric Analysis of Residential Water Demand," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 59(1), pages 137-166, September.
- 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 153-169, Suppl. De.
- Thierry Magnac & Jean-Marc Robin & Michael Visser, 1995. "Analysing incomplete individual employment histories using indirect inference," Post-Print hal-00359425, HAL.
- Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
- Kaplan, David M. & Sun, Yixiao, 2017. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," Econometric Theory, Cambridge University Press, vol. 33(01), pages 105-157, February.
- Kaplan, David M. & Sun, Yixiao, 2012. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," University of California at San Diego, Economics Working Paper Series qt888657tp, Department of Economics, UC San Diego.
- David M. Kaplan & Yixiao Sun, 2013. "Smoothed Estimating Equations for Instrumental Variables Quantile Regression," Working Papers 1314, Department of Economics, University of Missouri.
- 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.
- Lopez Garcia, Italo, 2015. "Human Capital and Labor Informality in Chile: A Life-Cycle Approach," Working Papers 1087, RAND Corporation.
- 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.
- Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-531, 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. Full references (including those not matched with items on IDEAS)