Advanced Search
MyIDEAS: Login to save this paper or follow this series

Bayesian Estimation of Dynamic Discrete Choice Models

Contents:

Author Info

  • Susumu Imai

    ()
    (Queen's University)

  • Neelam Jain

    ()
    (Northern Illinois University)

  • Andrew Ching

    ()
    (University of Toronto)

Abstract

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.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1118.pdf
File Function: First version 2006
Download Restriction: no

Bibliographic Info

Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1118.

as in new window
Length: 77 pages
Date of creation: Dec 2006
Date of revision:
Handle: RePEc:qed:wpaper:1118

Contact details of provider:
Postal: Kingston, Ontario, K7L 3N6
Phone: (613) 533-2250
Fax: (613) 533-6668
Email:
Web page: http://qed.econ.queensu.ca/
More information through EDIRC

Related research

Keywords: Bayesian Estimation; Dynamic Discrete Choice Model; Dynamic Programming; Markov Chain Monte Carlo; Bayesian Dynamic Programming Estimation;

Other versions of this item:

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

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.:
as in new window
  1. 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.
  2. Oliver LINTON, . "Applied nonparametric methods," Statistic und Oekonometrie 9312, Humboldt Universitaet Berlin.
  3. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
  4. repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
  5. Siddhartha Chib & Edward Greenberg, 1994. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometrics 9408001, EconWPA, revised 24 Oct 1994.
  6. Geweke, John & Houser, Dan & Keane, Michael, 1999. "Simulation Based Inference for Dynamic Multinomial Choice Models," MPRA Paper 54279, University Library of Munich, Germany.
  7. Härdle,Wolfgang, 1992. "Applied Nonparametric Regression," Cambridge Books, Cambridge University Press, number 9780521429504, April.
  8. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
  9. Hardle, W., 1992. "Applied Nonparametric Methods," Papers 9206, Tilburg - Center for Economic Research.
  10. Houser, Daniel, 2003. "Bayesian analysis of a dynamic stochastic model of labor supply and saving," Journal of Econometrics, Elsevier, vol. 113(2), pages 289-335, April.
  11. Andriy Norets, 2009. "Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables," Econometrica, Econometric Society, vol. 77(5), pages 1665-1682, 09.
  12. Lancaster, Tony, 1997. "Exact Structural Inference in Optimal Job-Search Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(2), pages 165-79, April.
  13. Imai, Susumu & Krishna, Kala, 2001. "Employment, Dynamic Deterrence and Crime," Working Papers 1-01-2, Pennsylvania State University, Department of Economics.
  14. 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.
  15. Susumu Imai & Michael P. Keane, 2004. "Intertemporal Labor Supply and Human Capital Accumulation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 601-641, 05.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:qed:wpaper:1118. 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: (Mark Babcock).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.