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

Hyeong Ho Mun; Eun Young Shim; Tae Hwan Kim

doi:10.1080/00949655.2012.754027

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

Hyeong Ho Mun, Eun Young Shim, Tae Hwan Kim

Department of Economics

Research output: Contribution to journal › Article › peer-review

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 language	English
Pages (from-to)	1552-1562
Number of pages	11
Journal	Journal of Statistical Computation and Simulation
Volume	84
Issue number	7
DOIs	https://doi.org/10.1080/00949655.2012.754027
Publication status	Published - 2014 Jul

Bibliographical note

Funding Information:
Tae-Hwan Kim is grateful for financial support from the National Research Foundation of Korea – a grant funded by the Korean Government (NRF-2009-327-B00088).

All Science Journal Classification (ASJC) codes

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

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10.1080/00949655.2012.754027

Cite this

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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.",

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

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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