Testing for Heteroskedasticity and Predictive Failure in Linear Regression Models

It is argued that, when researchers wish to carry out a Chow test of the significance of prediction errors, it is necessary to assume homoskedasticity because standard results on heteroskedasticity‐robust tests are not available. The effects of heteroskedasticity on the Chow prediction error test ar...

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Bibliographic Details
Published in:Oxford bulletin of economics and statistics 2008-06, Vol.70 (3), p.415-429
Main Author: Godfrey, L. G.
Format: Article
Language:English
Subjects:
C12
C52
Econometrics
Errors
Estimation
Forecasts
Linear models
Monte Carlo simulation
Regression analysis
Studies
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Summary:It is argued that, when researchers wish to carry out a Chow test of the significance of prediction errors, it is necessary to assume homoskedasticity because standard results on heteroskedasticity‐robust tests are not available. The effects of heteroskedasticity on the Chow prediction error test are examined. The implementation of tests for heteroskedasticity is discussed, with the case in which the regressors include dummy variables for prediction error tests receiving special attention. Monte Carlo results are reported.
ISSN:0305-9049
1468-0084
DOI:10.1111/j.1468-0084.2007.00498.x