A Mata Geweke–Hajivassiliou–Keane multivariate normal simulator

Richard Gates ()
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Richard Gates: StataCorp LP

Stata Journal, 2006, vol. 6, issue 2, 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
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:6:y:2006:i:2:p:190-213

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