Using bootstrap methods to obtain non-normality robust Chow prediction tests.

DOI10.1016/S0165-1765(02)00088-5zbMath1051.62032MaRDI QIDQ1608849

Chris D. Orme, Leslie G. Godfrey

Publication date: 13 August 2002

Published in: Economics Letters (Search for Journal in Brave)

zbMATH Keywords

BootstrapRegression modelsChow tests

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Parametric hypothesis testing (62F03) Robustness and adaptive procedures (parametric inference) (62F35) Bootstrap, jackknife and other resampling methods (62F40) Statistical methods; economic indices and measures (91B82)

Uses Software

• bootlib

Cites Work

• Unnamed Item
• Bootstrapping the Box-Pierce \(Q\) test: a robust test of uncorrelatedness
• Useful invariance results for generalized regression models
• Tests of Equality Between Sets of Coefficients in Two Linear Regressions
• Prepivoting Test Statistics: A Bootstrap View of Asymptotic Refinements
• Controlling the significance levels of prediction error tests for linear regression models

This page was built for publication: Using bootstrap methods to obtain non-normality robust Chow prediction tests.