A robust test for autocorrelation in the presence of a structural break in variance

Hyeong Ho Mun, Eun Young Shim, Tae Hwan Kim

Research output: Contribution to journalArticle

Abstract

It has been known that when there is a break in the variance (unconditional heteroskedasticity) of the error term in linear regression models, a routine application of the Lagrange multiplier (LM) test for autocorrelation can cause potentially significant size distortions. We propose a new test for autocorrelation that is robust in the presence of a break in variance. The proposed test is a modified LM test based on a generalized least squares regression. Monte Carlo simulations show that the new test performs well in finite samples and it is especially comparable to other existing heteroskedasticity-robust tests in terms of size, and much better in terms of power.

Original languageEnglish
Pages (from-to)1552-1562
Number of pages11
JournalJournal of Statistical Computation and Simulation
Volume84
Issue number7
DOIs
Publication statusPublished - 2014 Jul

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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