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	Provedor de dados AgEcon (2) Autor Gates, Richard (2) Drukker, David M. (1) Palavra-chave Research Methods/ Statistical Methods (2) GHK (1) Halton set (1) Halton() (1) Hammersley set (1) Importance sampling (1) Maximum simulated likelihood (1) Monte Carlo (1) Number-theoretic statistics (1) Quasirandom numbers (1) Quasi–Monte Carlo (1) Mais... Tipo do documento Journal Article (2) Ano 2006 (2) País United States (2) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última Generating Halton sequences using Mata AgEcon Drukker, David M.; Gates, Richard. This paper discusses the advantages of Halton sequences over pseudorandom uniform numbers when using simulation to approximate integrals numerically. We describe two types of sequences and give Mata examples. Finally, we document the Mata function halton(), currently in release 9.1 of Stata, which computes both a Halton sequence and its Hammersley variant. Options to use these point sets are available in the Stata 9 program asmprobit, a multinomial-probit estimator, and in the Stata 9.1 Mata function ghk(), the Geweke–Hajivassiliou–Keane multivariate-normal simulator. Tipo: Journal Article Palavras-chave: Halton(); Halton set; Hammersley set; Quasirandom numbers; Research Methods/ Statistical Methods. Ano: 2006 URL: http://purl.umn.edu/117570 A Mata Geweke–Hajivassiliou–Keane multivariate normal simulator AgEcon Gates, Richard. 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. Tipo: Journal Article Palavras-chave: GHK; Maximum simulated likelihood; Monte Carlo; Quasi–Monte Carlo; Importance sampling; Number-theoretic statistics; Research Methods/ Statistical Methods. Ano: 2006 URL: http://purl.umn.edu/117569 Registros recuperados: 2 Primeira ... 1 ... Última

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