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

作者：Mun Hyeong Ho; Shim Eun Young; Kim Tae Hwan^*

来源：Journal of Statistical Computation and Simulation, 2014, 84(7): 1552-1562.

DOI：10.1080/00949655.2012.754027

• LM test
• variance break
• variance-ratio test
• GLS regression

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.

• 出版日期2014-7-3