Topic
Random effects model
About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.
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TL;DR: A survey of the methods used in the estimation of limited dependent variable models with panel data is presented in this article, where the problems of fixed effects vs. random effects and serious correlation vs. state dependence are discussed with reference to continuous data.
Abstract: This paper presents a survey of the methods used in the estimation of limited dependent variable models with panel data. It first reviews some issues in the analysis of panel data when the dependent variables are continuous. The problems of fixed effects vs. random effects and serious correlation vs. state dependence are discussed with reference to continuous data. The paper then discusses these problems with reference to the panel logit, panel probit, and panel tobit models. The paper presents a comparative assessment of these models.
482 citations
TL;DR: In this article, the authors extend the two-part regression approach to longitudinal settings by introducing random coefficients into both the logistic and the linear stages, and obtain maximum likelihood estimates for the fixed coefficients and variance components by an approximate Fisher scoring procedure based on high-order Laplace approximations.
Abstract: A semicontinuous variable has a portion of responses equal to a single value (typically 0) and a continuous, often skewed, distribution among the remaining values. In cross-sectional analyses, variables of this type may be described by a pair of regression models; for example, a logistic model for the probability of nonzero response and a conditional linear model for the mean response given that it is nonzero. We extend this two-part regression approach to longitudinal settings by introducing random coefficients into both the logistic and the linear stages. Fitting a two-part random-effects model poses computational challenges similar to those found with generalized linear mixed models. We obtain maximum likelihood estimates for the fixed coefficients and variance components by an approximate Fisher scoring procedure based on high-order Laplace approximations. To illustrate, we apply the technique to data from the Adolescent Alcohol Prevention Trial, examining reported recent alcohol use for students in g...
475 citations
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TL;DR: In this paper, a Stata-specific treatment of generalized linear mixed models, also known as multilevel or hierarchical models, is presented, which allow fixed and random effects and are appropriate not only for continuous Gaussian responses but also for binary, count, and other types of limited dependent variables.
Abstract: This text is a Stata-specific treatment of generalized linear mixed models, also known as multilevel or hierarchical models. These models are "mixed" in the sense that they allow fixed and random effects and are "generalized" in the sense that they are appropriate not only for continuous Gaussian responses but also for binary, count, and other types of limited dependent variables.
474 citations
TL;DR: It is concluded that likelihood based methods are preferred to the standard method in undertaking random effects meta-analysis when the value of sigma B2 has an important effect on the overall estimated treatment effect.
Abstract: In a meta-analysis of a set of clinical trials, a crucial but problematic component is providing an estimate and confidence interval for the overall treatment effect theta. Since in the presence of heterogeneity a fixed effect approach yields an artificially narrow confidence interval for theta, the random effects method of DerSimonian and Laird, which incorporates a moment estimator of the between-trial components of variance sigma B2, has been advocated. With the additional distributional assumptions of normality, a confidence interval for theta may be obtained. However, this method does not provide a confidence interval for sigma B2, nor a confidence interval for theta which takes account of the fact that sigma B2 has to be estimated from the data. We show how a likelihood based method can be used to overcome these problems, and use profile likelihoods to construct likelihood based confidence intervals. This approach yields an appropriately widened confidence interval compared with the standard random effects method. Examples of application to a published meta-analysis and a multicentre clinical trial are discussed. It is concluded that likelihood based methods are preferred to the standard method in undertaking random effects meta-analysis when the value of sigma B2 has an important effect on the overall estimated treatment effect.
471 citations
TL;DR: In this article, several lagrange multiplier (LM) tests for the panel data regression model with spatial error correlation are presented. But the authors do not consider the presence of random regional effects.
467 citations