Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation
AbstractWe 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.
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Bibliographic InfoPaper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 584.
Length: 37 p.
Date of creation: 2006
Date of revision:
Simulation estimation; maximum simulated likelihood; multivariate probit; Halton sequences; pseudo-random sequences; multivariate normal; GHK simulator;
Other versions of this item:
- Lorenzo Cappellari & Stephen P. Jenkins, 2006. "Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation," Stata Journal, StataCorp LP, vol. 6(2), pages 156-189, June.
- Cappellari, Lorenzo & Jenkins, Stephen P., 2006. "Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation," IZA Discussion Papers 2112, Institute for the Study of Labor (IZA).
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
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