Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation

Lorenzo Cappellari and Stephen Jenkins ()

No 2112, IZA Discussion Papers from Institute for the Study of Labor (IZA)

Abstract: We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata programs for this purpose: -mdraws- for deriving draws from the standard uniform density using either Halton or pseudo-random sequences, and an egen function -mvnp()- for calculating the probabilities themselves. Several illustrations show how the programs may be used for maximum simulated likelihood estimation.

Keywords: simulation estimation; maximum simulated likelihood; multivariate probit; Halton sequences; pseudo-random sequences; multivariate normal; GHK simulator (search for similar items in EconPapers)
JEL-codes: C15 C51 C87 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dcm and nep-ecm
Date: 2006-05
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Related works:
Working Paper: Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation (2006)
Working Paper: Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation (2006)
Journal Article: Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation (2006)
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