The Method of Simulated Scores for the Estimation of LDV Models
The method of simulated scores (MSS) is presented for estimating limited dependent variables models (LDV) with flexible correlation structure in the unobservables. The authors propose simulators that are continuous in the unknown parameter vectors, and hence standard optimization methods can be used to compute the MSS estimators that employ these simulators. The first continuous method relies on a recursive conditioning of the multivariate normal density through a Cholesky triangularization of its variance-covariance matrix. The second method combines results about the conditionals of the multivariate normal distribution with Gibbs resampling techniques. The authors establish consistency and asymptotic normality of the MSS estimators and derive suitable rates at which the number of simulations must rise if biased simulators are used.
(This abstract was borrowed from another version of this item.)
|Date of creation:||Apr 1993|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (203) 432-3576
Fax: (203) 432-5779
Web page: http://www.econ.yale.edu/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:wop:yaluwp:_023. 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: (Thomas Krichel)
If references are entirely missing, you can add them using this form.