Testing for serial correlation in hierarchical linear models

This paper proposes a simple hierarchical model and a testing strategy to identify intra-cluster correlations, in the form of nested random effects and serially correlated error components. We focus on intra-cluster serial correlation at different nested levels, a topic that has not been studied in the literature before. A Neyman's C(α) framework is used to derive LM-type tests that allow researchers to identify the appropriate level of clustering as well as the type of intra-group correlation. An extensive Monte Carlo exercise shows that the proposed tests perform well in finite samples and under non-Gaussian distributions. Fil: Alejo, Osvaldo Javier. Universidad Nacional de La Plata. Facultad de Ciencias Económicas. Departamento de Ciencias Económicas. Centro de Estudios Distributivos Laborales y Sociales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Montes Rojas, Gabriel Victorio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Interdisciplinario de Economía Política de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires; Argentina. Universidad de Barcelona; España