A robust test for autocorrelation in the presence of a structural break in variance
| Autor: | Hyeong Ho Mun, Tae-Hwan Kim, Eun Young Shim |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Statistics and Probability
Heteroscedasticity Breusch–Pagan test Applied Mathematics Autoregressive conditional heteroskedasticity Omnibus test Structural break White test Generalized least squares Modeling and Simulation Statistics Statistics Probability and Uncertainty Mathematics Variance function |
| Zdroj: | Journal of Statistical Computation and Simulation. 84:1552-1562 |
| ISSN: | 1563-5163 0094-9655 |
| DOI: | 10.1080/00949655.2012.754027 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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