EconPapers    
Economics at your fingertips  
 

Heterogeneity in dynamic discrete choice models

Martin Browning and Jesus Carro ()

Econometrics Journal, 2010, vol. 13, issue 1, pages 1-39

Abstract: We consider dynamic discrete choice models with heterogeneity in both the levels parameter and the state dependence parameter. We first present an empirical analysis that motivates the theoretical analysis which follows. The theoretical analysis considers a simple two-state, first-order Markov chain model without covariates in which both transition probabilities are heterogeneous. Using such a model we are able to derive exact small sample results for bias and mean squared error (MSE). We discuss the maximum likelihood approach and derive two novel estimators. The first is a bias corrected version of the Maximum Likelihood Estimator (MLE) although the second, which we term MIMSE, minimizes the integrated mean square error. The MIMSE estimator is always well defined, has a closed-form expression and inherits the desirable large sample properties of the MLE. Our main finding is that in almost all short panel contexts the MIMSE significantly outperforms the other two estimators in terms of MSE. A final section extends the MIMSE estimator to allow for exogenous covariates. Copyright (C) The Author(s). Journal compilation (C) Royal Economic Society 2010.

Date: 2010
References: Add references at CitEc
Citations View citations in EconPapers (10) Track citations by RSS feed

Downloads: (external link)
http://www.blackwell-synergy.com/doi/abs/10.1111/j.1368-423X.2009.00301.x link to full text (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Heterogeneity in dynamic discrete choice models (2006) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:ect:emjrnl:v:13:y:2010:i:1:p:1-39

Ordering information: This journal article can be ordered from
http://www.ectj.org

Access Statistics for this article

Econometrics Journal is currently edited by Richard J. Smith, Oliver Linton, Pierre Perron, Jaap Abbring and Marius Ooms

More articles in Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Series data maintained by Wiley-Blackwell Digital Licensing ().

 
Page updated 2017-03-26
Handle: RePEc:ect:emjrnl:v:13:y:2010:i:1:p:1-39