scholarly journals Maximum Likelihood and Two-Step Estimation of an Ordered-Probit Selection Model

2007 ◽  
Vol 7 (2) ◽  
pp. 167-182 ◽  
Author(s):  
Richard Chiburis ◽  
Michael Lokshin
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Monte Carlo Simulations ◽  
Regression Model ◽  
Selection Rule ◽  
Ordered Probit ◽  
Maximum Likelihood Estimates ◽  
Full Information ◽  
Full Information Maximum Likelihood ◽  
Two Step Estimation

We discuss the estimation of a regression model with an ordered-probit selection rule. We have written a Stata command, oheckman, that computes two-step and full-information maximum-likelihood estimates of this model. Using Monte Carlo simulations, we compare the performances of these estimators under various conditions.

scholarly journals Maximum Likelihood Estimation of the Parameters of a System of Simultaneous Regression Equations

1988 ◽  
Vol 4 (1) ◽  
pp. 159-170 ◽  
Author(s):  
James Durbin
Keyword(s):  
Maximum Likelihood ◽  
Iterative Procedure ◽  
Least Squares Method ◽  
Likelihood Estimation ◽  
Maximum Likelihood Estimates ◽  
Regression Equations ◽  
Full Information ◽  
Full Information Maximum Likelihood ◽  
Three Stage Least Squares ◽  
Newton Raphson

Procedures for computing the full information maximum likelihood (FIML) estimates of the parameters of a system of simultaneous regression equations have been described by Koopmans, Rubin, and Leipnik, Chernoff and Divinsky, Brown, and Eisenpress. However, all of these methods are rather complicated since they are based on estimating equations that are expressed in an inconvenient form. In this paper, a transformation of the maximum likelihood (ML) equations is developed which not only leads to simpler computations but which also simplifies the study of the properties of the estimates. The equations are obtained in a form which is capable of solution by a modified Newton-Raphson iterative procedure. The form obtained also shows up very clearly the relation between the maximum likelihood estimates and those obtained by the three-stage least squares method of Zellner and Theil.

scholarly journals The normal-tangent-G class of probabilistic distributions: properties and real data modellin

2020 ◽  
pp. 827-838
Author(s):  
Fábio Silveira ◽  
Frank Gomes-Silva ◽  
Cícero Brito ◽  
Moacyr Cunha-Filho ◽  
Jader Jale ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Probability Density Function ◽  
Monte Carlo Simulations ◽  
Probability Distributions ◽  
Series Representation ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Special Cases ◽  
Probability Density Function Pdf

This paper introduces a novel class of probability distributions called normal-tangent-G, whose submodels are parsi- monious and bring no additional parameters besides the baseline’s. We demonstrate that these submodels are iden- tifiable as long as the baseline is. We present some properties of the class, including the series representation of its probability density function (pdf) and two special cases. Monte Carlo simulations are carried out to study the behav- ior of the maximum likelihood estimates (MLEs) of the parameters for a particular submodel. We also perform an application of it to a real dataset to exemplify the modelling benefits of the class.

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