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

Lorenzo Cappellari and Stephen Jenkins ()

No 584, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

Abstract: We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata programs for this purpose: mvdraws 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)
New Economics Papers: this item is included in nep-ecm
Date: 2006
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Related works:
Journal Article: Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation (2006) Downloads
Journal Article: Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation (2006) Downloads
Working Paper: Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation (2006) Downloads
Working Paper: Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation (2006) Downloads
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