Published August 7, 2017
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Estimating Dynamic Discrete Choice Models Via Convex Analysis
- Creators
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Chiong, Khai X.
- Galichon, Alfred
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Shum, Matthew
Abstract
Using results from convex analysis, we characterize the identification and estimation of dynamic discrete-choice models based on the random utility framework. Based on these insights, we propose a new two-step estimator for these models, which is easily applicable to models in which the utility shocks may not derive from an extreme- value distribution, and may be mutually correlated with each other and with the state variables. Monte Carlo results demonstrate the good performance of this estimator, and we provide a short application using the dynamic bus engine replacement model in Rust (1987).
Additional Information
The authors thank Thierry Magnac for helpful comments, and John Rust for his data. Galichon's research has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement n◦313699 and from FiME, Laboratoire de Finance des Marchés de l'Energie (www.fime-lab.org).Attached Files
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Additional details
- Eprint ID
- 79479
- Resolver ID
- CaltechAUTHORS:20170727-090045366
- European Research Council (ERC)
- Laboratoire de Finance des Marchés de l'Energie
- Created
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2017-08-07Created from EPrint's datestamp field
- Updated
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2020-03-09Created from EPrint's last_modified field
- Caltech groups
- Social Science Working Papers
- Series Name
- Social Science Working Paper
- Series Volume or Issue Number
- 1374