A Simulation Estimator for Dynamic Models of Discrete Choice
This paper analyses a new estimator for the structural parameters of dynamic models of discrete choice. Based on an inversion theorem due to Hotz and Miller (1993), which establishes the existence of a one-to-one mapping between the conditional valuation functions for the dynamic problem and their associated conditional choice probabilities, we exploit simulation techniques to estimate models which do not possess terminal states. In this way our Conditional Choice Simulation (CCS) estimator complements the Conditional Choice Probability (CCP) estimator of Hotz and Miller (1993). Drawing on work in empirical process theory by Pakes and Pollard (1989), we establish its large sample properties, and then conduct a Monte Carlo study of Rust's (1987) model of bus engine replacement to compare its small sample properties with those of Maximum Likelihood (ML).
(This abstract was borrowed from another version of this item.)
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||1992|
|Date of revision:|
|Contact details of provider:|| Postal: Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890|
Web page: http://www.tepper.cmu.edu/
|Order Information:||Web: http://student-3k.tepper.cmu.edu/gsiadoc/GSIA_WP.asp|
When requesting a correction, please mention this item's handle: RePEc:cmu:gsiawp:1992-13. 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: (Steve Spear)
If references are entirely missing, you can add them using this form.