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Monte Carlo methodology for LM and LR autocorrelation tests in multivariate regression

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  • DESCHAMPS, P. J.

Abstract

The small sample critical values of three test statistics for vector autocorrelated errors are investigated in the context of several specific empirical models. Under the null hypothesis, two of the proposed statistics do not depend on nuisance parameters when the regressors are strongly exogenous, and their distributions are easy to estimate. We also propose a simple and accurate size correction for the Chi-square likelihood ratio test
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Suggested Citation

  • Deschamps, P. J., 1996. "Monte Carlo methodology for LM and LR autocorrelation tests in multivariate regression," LIDAM Reprints CORE 1234, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:1234
    Note: In : Annales d'Economie et de Statistique, 43, 149-169, 1996
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    Cited by:

    1. Psaradakis, Zacharias & Vávra, Marián, 2014. "On testing for nonlinearity in multivariate time series," Economics Letters, Elsevier, vol. 125(1), pages 1-4.
    2. Jean-Marie Dufour & Lynda Khalaf & Marcel Voia, 2013. "Finite-sample resampling-based combined hypothesis tests, with applications to serial correlation and predictability," CIRANO Working Papers 2013s-40, CIRANO.
    3. DUFOUR, Jean-Marie & KHALAF, Lynda & BEAULIEU, Marie-Claude, 2003. "Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models," Cahiers de recherche 2003-08, Universite de Montreal, Departement de sciences economiques.
    4. Marian Vavra, 2013. "Testing for linear and Markov switching DSGE models," Working and Discussion Papers WP 3/2013, Research Department, National Bank of Slovakia.
    5. Deschamps, Philippe J., 2000. "Exact small-sample inference in stationary, fully regular, dynamic demand models," Journal of Econometrics, Elsevier, vol. 97(1), pages 51-91, July.

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