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	Provedor de dados AgEcon (6) Autor Cappellari, Lorenzo (1) Deb, Partha (1) Gates, Richard (1) Haan, Peter (1) Hole, Arne Risa (1) Jenkins, Stephen P. (1) Stewart, Mark B. (1) Trivedi, Pravin K. (1) Uhlendorff, Arne (1) Mais... Palavra-chave Maximum simulated likelihood (6) Research Methods/ Statistical Methods (6) Halton sequences (4) Simulation estimation (2) Autocorrelated errors (1) Count data (1) Egen function mvnp() (1) GHK (1) GHK simulator (1) Importance sampling (1) Latent factors (1) Mdraws (1) Mixed logit (1) Mixlcov (1) Mixlogit (1) Mixlpred (1) Monte Carlo (1) Mtreatnb (1) Multinomial logistic (1) Multinomial logistic model (1) Mais... Tipo do documento Journal Article (5) Article (1) Ano 2007 (1) 2006 (5) País United States (6) Idioma Inglês (6)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 6 Primeira ... 1 ... Última Fitting mixed logit models by using maximum simulated likelihood AgEcon Hole, Arne Risa. This article describes the mixlogit Stata command for fitting mixed logit models by using maximum simulated likelihood. Tipo: Article Palavras-chave: Mixlogit; Mixlpred; Mixlcov; Mixed logit; Maximum simulated likelihood; Research Methods/ Statistical Methods. Ano: 2007 URL: http://purl.umn.edu/119283 Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation AgEcon Cappellari, Lorenzo; Jenkins, Stephen P.. 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 themselves. Several illustrations show how the programs may be used for maximum simulated likelihood estimation. Tipo: Journal Article Palavras-chave: Mdraws; Egen function mvnp(); Simulation estimation; Maximum simulated likelihood; Multivariate probit; Halton sequences; Pseudorandom sequences; Multivariate normal; GHK simulator; Research Methods/ Statistical Methods. Ano: 2006 URL: http://purl.umn.edu/117568 Maximum simulated likelihood estimation of random–effects dynamic probit models with autocorrelated errors AgEcon Stewart, Mark B.. This paper investigates using maximum simulated likelihood (MSL) estimation for random-effects dynamic probit models with autocorrelated errors. It presents and illustrates a new Stata command, redpace, for this estimator. The paper also compares using pseudorandom numbers and Halton sequences of quasirandom numbers for MSL estimation of these models. Tipo: Journal Article Palavras-chave: Redpace; Simulation estimation; Maximum simulated likelihood; Halton sequences; Autocorrelated errors; Research Methods/ Statistical Methods. Ano: 2006 URL: http://purl.umn.edu/117576 Maximum simulated likelihood estimation of a negative binomial regression model with multinomial endogenous treatment AgEcon Deb, Partha; Trivedi, Pravin K.. We describe specification and estimation of a multinomial treatment effects negative binomial regression model. A latent factor structure is used to accommodate selection into treatment, and a simulated likelihood method is used for estimation. We describe its implementation via the mtreatnb command. Tipo: Journal Article Palavras-chave: Mtreatnb; Multinomial treatment effects; Latent factors; Count data; Negative binomial; Multinomial logit; Multinomial logistic; Halton sequences; Maximum simulated likelihood; Research Methods/ Statistical Methods. Ano: 2006 URL: http://purl.umn.edu/117575 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 Estimation of multinomial logit models with unobserved heterogeneity using maximum simulated likelihood AgEcon Haan, Peter; Uhlendorff, Arne. In this paper, we suggest a Stata routine for multinomial logit models with unobserved heterogeneity using maximum simulated likelihood based on Halton sequences. The purpose of this paper is twofold. First, we describe the technical implementation of the estimation routine and discuss its properties. Further, we compare our estimation routine with the Stata program gllamm, which solves integration by using Gauss–Hermite quadrature or adaptive quadrature. For the analysis, we draw on multilevel data about schooling. Our empirical findings show that the estimation techniques lead to approximately the same estimation results. The advantage of simulation over Gauss–Hermite quadrature is a marked reduction in computational time for integrals with higher... Tipo: Journal Article Palavras-chave: Multinomial logit model; Multinomial logistic model; Panel data; Unobserved heterogeneity; Maximum simulated likelihood; Halton sequences; Research Methods/ Statistical Methods. Ano: 2006 URL: http://purl.umn.edu/117572 Registros recuperados: 6 Primeira ... 1 ... Última

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