situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.

to the estimation of a labour demand model using company panel data.

This paper considers bootstrap methods for panel data models with fixed regressors. It is shown that simple resampling methods (i.i.d., individual only or temporal only) are not always valid in simple cases of interest, while a double resampling that combines resampling in both individual

These notes summarize some recent, and perhaps not-so-recent, advances in the estimation of nonlinear panel data models. Research in the last 10 to 15 years has branched off in two directions. In one, the focus has been on parameter estimation, possibly only up to a common scale factor

These notes cover some recent topics in linear panel data models. They begin with a “modern ” treatment of the basic linear model, and then consider some embellishments, such as random slopes and time-varying factor loads. In addition, fully robust tests for correlated random effects, lack

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. American Economist and Omicron Delta Epsilon are collaborating with JSTOR to digitize, preserve and extend access to The American Economist.

This article provides a survey of the specification and estimation of spatial panel data models. These models include spatial error autocorrelation, or the specification is extended with a spa-tially lagged dependent variable. In particular, the author focuses on the specification and esti

Abstract not found

In this paper we consider a panel data model with error components that are both spatially and time-wise correlated. The model blends specifications typically considered in the spatial literature with those considered in the error components literature. We introduce generalizations

We propose tests for normality of the error components in panel data models, using estimates of skewness and kurtosis in the components. JEL classification: C33