Bayesian Estimation of Dynamic Discrete Choice Models

Arbeitspapier

We propose a new methodology for structural estimation of dynamic discrete choice models. We combine the Dynamic Programming (DP) solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though per solution-estimation iteration, the number of grid points on the state variable is small, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the "Curse of Dimensionality". We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values.

Objekt anzeigen

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Queen's Economics Department Working Paper ; No. 1118

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Model Construction and Estimation ; Optimization Techniques; Programming Models; Dynamic Analysis ; Computational Techniques; Simulation Modeling ; Industrial Organization: General
• Thema: Bayesian Estimation ; Dynamic Discrete Choice Model ; Dynamic Programming ; Markov Chain Monte Carlo ; Bayesian Dynamic Programming Estimation ; Diskrete Entscheidung ; Dynamische Optimierung ; Markov-Kette ; Bayes-Statistik ; Experiment ; Theorie

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Imai, Susumu ; Jain, Neelam ; Ching, Andrew
• Ereignis: Veröffentlichung
• (wer): Queen's University, Department of Economics
• (wo): Kingston (Ontario)
• (wann): 2006

Weitere Informationen

• Handle: http://hdl.handle.net/10419/189394