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Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation Author info | Abstract | Publisher info | Download info | Related research | Statistics Lorenzo Cappellari () (Catholic University of Milan, University of Essex)
Stephen P. Jenkins () (University of Essex)
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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 pseudorandom sequences, and an egen function, mvnp(), for calculating the probabilities them- selves. Several illustrations show how the programs may be used for maximum simulated likelihood estimation. Copyright 2006 by StataCorp LP.
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Article provided by StataCorp LP in its journal Stata Journal .
Volume (Year): 6 (2006)
Issue (Month): 2 (June)
Pages: 156-189
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Handle: RePEc:tsj:stataj:v:6:y:2006:i:2:p:156-189Contact details of provider: Web page: http://www.stata-journal.com/
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Keywords: mdraws ; egen function mvnp( ) ; simulation estimation ; maxi- mum simulated likelihood ; multivariate probit ; Halton sequences ; pseudorandom sequences ; multivariate normal ; GHK simulator ; Other versions of this item:
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.: William W. Gould & Jeffrey Pitblado & William Sribney, 2006.
"Maximum Likelihood Estimation with Stata ,"
Stata Press books ,
StataCorp LP,
edition 3, number ml3, November-.
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Lorenzo Cappellari & Stephen P. Jenkins, 2003.
"Multivariate probit regression using simulated maximum likelihood ,"
United Kingdom Stata Users' Group Meetings 2003
10, Stata Users Group.
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