A Mata Geweke–Hajivassiliou–Keane multivariate normal simulator
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.
Year of publication: |
2006
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Authors: | Gates, Richard |
Published in: |
Stata Journal. - StataCorp LP. - Vol. 6.2006, 2, p. 190-213
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Publisher: |
StataCorp LP |
Subject: | GHK | maximum simulated likelihood | Monte Carlo | quasi-Monte Carlo | importance sampling | number-theoretic statistics |
Saved in:
freely available
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