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Another Look At The Estimation Of Dynamic Programming Models With Censored Decision Variables

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  • Rocío Sánchez-Mangas

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Abstract

In this paper we propose a new approach to estimate the structural parameters in the context of a censored continuous decision model. Instead of handling with the original model, we consider an approximate model in which the decision variable has been discretized in a finite number of values. In this sense, an ordered choice model becomes a natural approximation to an underlying and more complicated censored continuous one. We extend the kind of Hotz-Miller estimators proposed for the estimation of binary or multinomial choice models to the context of ordered choice models. The estimation approach is based on the existence of a one-to-one mapping from conditional choice value functions to conditional choice probabilities. Exploiting the invertibility of that mapping it is possible to obtain structural parameter estimates without solving the dynamic programming problem.

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Bibliographic Info

Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws022404.

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Date of creation: Jun 2002
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Handle: RePEc:cte:wsrepe:ws022404

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  1. Aguirregabiria, V., 1997. "Estimation of Dynamic Programming Models with Censored Dependent Variables," UWO Department of Economics Working Papers 9711, University of Western Ontario, Department of Economics.
  2. V. Joseph Hotz & Robert A. Miller & Seth Sanders & Jeffrey Smith, 1992. "A Simulation Estimator for Dynamic Models of Discrete Choice," Working Papers 9205, Harris School of Public Policy Studies, University of Chicago.
  3. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
  4. Victor Aguirregabiria & Cesar Alonso-Borrego, 2009. "Labor Contracts and Flexibility: Evidence from a Labor Market Reform in Spain," Working Papers tecipa-346, University of Toronto, Department of Economics.
  5. Rust, John, 1996. "Numerical dynamic programming in economics," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 14, pages 619-729 Elsevier.
  6. Victor Aguirregabiria & Pedro Mira, 2002. "Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models," Econometrica, Econometric Society, vol. 70(4), pages 1519-1543, July.
  7. Zvi Eckstein & Kenneth I. Wolpin, 1989. "The Specification and Estimation of Dynamic Stochastic Discrete Choice Models: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 24(4), pages 562-598.
  8. Michael P. Keane & Kenneth I. Wolpin, 1994. "The solution and estimation of discrete choice dynamic programming models by simulation and interpolation: Monte Carlo evidence," Staff Report 181, Federal Reserve Bank of Minneapolis.
  9. Hotz, V.J. & Miller, R.A., 1991. "Conditional Choice Probabilities and the Estimation of Dynamic Models," GSIA Working Papers 1992-12, Carnegie Mellon University, Tepper School of Business.
  10. repec:att:wimass:9106 is not listed on IDEAS
  11. Aguirregabiria, Victor, 1999. "The Dynamics of Markups and Inventories in Retailing Firms," Review of Economic Studies, Wiley Blackwell, vol. 66(2), pages 275-308, April.
  12. Ariel Pakes, 1991. "Dynamic Structural Models: Problems and Prospects. Mixed Continuous Discrete Controls and Market Interactions," Cowles Foundation Discussion Papers 984, Cowles Foundation for Research in Economics, Yale University.
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