### Abstract

We propose a new methodology for structural estimation of infinite horizon 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 the number of grid points on the state variable is small per solution-estimation iteration, 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.

Original language | English (US) |
---|---|

Pages (from-to) | 1865-1899 |

Number of pages | 35 |

Journal | Econometrica |

Volume | 77 |

Issue number | 6 |

DOIs | |

State | Published - Nov 1 2009 |

Externally published | Yes |

### Fingerprint

### Keywords

- Bayesian estimation
- Discrete choice models
- Dynamic programming
- Markov chain Monte Carlo

### ASJC Scopus subject areas

- Economics and Econometrics

### Cite this

*Econometrica*,

*77*(6), 1865-1899. https://doi.org/10.3982/ECTA5658

**Bayesian estimation of dynamic discrete choice models.** / Imai, Susumu; Jain, Neelam; Ching, Andrew.

Research output: Contribution to journal › Article

*Econometrica*, vol. 77, no. 6, pp. 1865-1899. https://doi.org/10.3982/ECTA5658

}

TY - JOUR

T1 - Bayesian estimation of dynamic discrete choice models

AU - Imai, Susumu

AU - Jain, Neelam

AU - Ching, Andrew

PY - 2009/11/1

Y1 - 2009/11/1

N2 - We propose a new methodology for structural estimation of infinite horizon 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 the number of grid points on the state variable is small per solution-estimation iteration, 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.

AB - We propose a new methodology for structural estimation of infinite horizon 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 the number of grid points on the state variable is small per solution-estimation iteration, 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.

KW - Bayesian estimation

KW - Discrete choice models

KW - Dynamic programming

KW - Markov chain Monte Carlo

UR - http://www.scopus.com/inward/record.url?scp=70349743297&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70349743297&partnerID=8YFLogxK

U2 - 10.3982/ECTA5658

DO - 10.3982/ECTA5658

M3 - Article

VL - 77

SP - 1865

EP - 1899

JO - Econometrica

JF - Econometrica

SN - 0012-9682

IS - 6

ER -