A Mata Geweke–Hajivassiliou–Keane multivariate normal simulator
Abstract. An accurate and efficient numerical approximation of the multivariate normal
(MVN) distribution function is necessary for obtaining maximum likelihood
estimates for models involving the MVN distribution. Numerical integration
through simulation (Monte Carlo) or number-theoretic (quasi–Monte
Carlo) techniques 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.
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Richard Gates
View all articles with these keywords:
GHK, maximum simulated likelihood, Monte Carlo, quasi–Monte Carlo, importance sampling, number-theoretic statistics
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