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| Miranda, Alfonso; Rabe-Hesketh, Sophia. |
| Studying behavior in economics, sociology, and statistics often involves fitting models in which the response variable depends on a dummy variable—also known as a regime-switch variable—or in which the response variable is observed only if a particular selection condition is met. In either case, standard regression techniques deliver inconsistent estimators if unobserved factors that affect the response are correlated with unobserved factors that affect the switching or selection variable. Consistent estimators can be obtained by maximum likelihood estimation of a joint model of the outcome and switching or selection variable. This article describes a “wrapper” program, ssm, that calls gllamm (Rabe-Hesketh, Skrondal, and Pickles, GLLAMM Manual [University... |
| Tipo: Journal Article |
Palavras-chave: Endogenous switching; Sample selection; Binary variable; Count data; Ordinal variable; Probit; Poisson regression; Adaptive quadrature; Gllamm; Wrapper; Ssm; Research Methods/ Statistical Methods. |
| Ano: 2006 |
URL: http://purl.umn.edu/117582 |
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| Rabe-Hesketh, Sophia; Skrondal, Anders; Pickles, Andrew. |
| Generalized linear models with covariate measurement error can be estimated by maximum likelihood using gllamm, a program that fits a large class of multilevel latent variable models (Rabe-Hesketh, Skrondal, and Pickles 2004). The program uses adaptive quadrature to evaluate the log likelihood, producing more reliable results than many other methods (Rabe-Hesketh, Skrondal, and Pickles 2002). For a single covariate measured with error (assuming a classical measurement model), we describe a “wrapper” command, cme, that calls gllamm to estimate the model. The wrapper makes life easy for the user by accepting a simple syntax and data structure and producing extended and easily interpretable output. The commands for preparing the data and running gllamm can... |
| Tipo: Journal Article |
Palavras-chave: Covariate measurement error; Measurement model; Congeneric measurement model; Factor model; Adaptive quadrature; Nonparametric maximum likelihood; NPMLE; Latent class model; Empirical Bayes; Simulation; Wrapper; Sensitivity analysis; Gllamm; Cme; Research Methods/ Statistical Methods. |
| Ano: 2003 |
URL: http://purl.umn.edu/116185 |
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