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Testing for serial correlation in hierarchical linear models

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

Abstract

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.

Suggested Citation

  • Alejo, Javier & Montes-Rojas, Gabriel & Sosa-Escudero, Walter, 2018. "Testing for serial correlation in hierarchical linear models," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 101-116.
  • Handle: RePEc:eee:jmvana:v:165:y:2018:i:c:p:101-116
    DOI: 10.1016/j.jmva.2017.11.007
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    Keywords

    Clusters; Random effects; Serial correlation;

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