Testing panel data regression models with spatial error correlation
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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.About:
This article is published in Journal of Econometrics.The article was published on 2003-11-01 and is currently open access. It has received 467 citations till now. The article focuses on the topics: Spatial econometrics & Random effects model.read more
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Book
Spatial Econometrics: Methods and Models
TL;DR: In this article, a typology of Spatial Econometric Models is presented, and the maximum likelihood approach to estimate and test Spatial Process Models is proposed, as well as alternative approaches to Inference in Spatial process models.
Book
Analysis of Panel Data
TL;DR: In this paper, the authors propose a homogeneity test for linear regression models (analysis of covariance) and show that linear regression with variable intercepts is more consistent than simple regression with simple intercepts.
Journal ArticleDOI
The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics
Trevor Breusch,Adrian Pagan +1 more
TL;DR: The Lagrange multiplier (LM) statistic as mentioned in this paper is based on the maximum likelihood ratio (LR) procedure and is used to test the effect on the first order conditions for a maximum of the likelihood of imposing the hypothesis.
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Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data
John C. Driscoll,Aart Kraay +1 more
TL;DR: The authors presented conditions under which a simple extension of common nonparametric covariance matrix estimation techniques yields standard error estimates that are robust to very general forms of spatial and temporal dependence as the time dimension becomes large.
Journal ArticleDOI
Growth Empirics: A Panel Data Approach
TL;DR: In this article, a panel data approach is advocated and implemented for studying growth convergence, and the familiar equation for testing convergence is reformulated as a dynamic panel data model, and different panel data estimators are used to estimate it.