Testing for serial correlation in hierarchical linear models

Javier Alejo, Gabriel Montes-Rojas, Walter Sosa-Escudero

Research output: Contribution to journalArticleResearchpeer-review


© 2017 Elsevier Inc. 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.
Original languageEnglish
Pages (from-to)101-116
JournalJournal of Multivariate Analysis
Publication statusPublished - 1 May 2018


  • Clusters
  • Random effects
  • Serial correlation

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