A Mata Geweke–Hajivassiliou–Keane multivariate normal simulator

Richard Gates ()
Additional contact information
Richard Gates: StataCorp LP

Stata Journal, 2006, vol. 6, issue 2, pages 190-213

Abstract: An accurate and efficient numerical approximation of the multivariate normal (MVN) distribution function is necessary for obtaining maximum likeli- hood estimates for models involving the MVN distribution. Numerical integration through simulation (Monte Carlo) or number-theoretic (quasi-Monte Carlo) tech- niques is one way to accomplish this task. One popular simulation technique is the Geweke-Hajivassiliou-Keane MVN simulator. This paper reviews this technique and introduces a Mata function that implements it. It also computes analytical first-order derivatives of the simulated probability with respect to the variables and the variance – covariance parameters. Copyright 2006 by StataCorp LP.

Keywords: GHK; maximum simulated likelihood; Monte Carlo; quasi-Monte Carlo; importance sampling; number-theoretic statistics (search for similar items in EconPapers)
Date: 2006

Downloads: (external link)
http://www.stata-journal.com/article.html?article=st0102
http://www.stata-journal.com/software/sj6-2/st0102/ (text/html)

Related works:
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:tsj:stataj:v:6:y:2006:i:2:p:190-213

Ordering information: This journal article can be ordered from
http://www.stata-journal.com/subscription.html

Access Statistics for this article

Stata Journal is edited by H. Joseph Newton and Nicholas J. Cox

More articles in Stata Journal from StataCorp LP
Series data maintained by Christopher F. Baum ().