Kernel smoothing end of sample instability tests P values
A Monte Carlo investigation shows that the rejection probability of the structural stability test of Andrews (2003) depends on several characteristics of the DGP, one of which is the length of the hypothesized break period. This is analyzed and found to be caused, at least in part, by the fact that the number of subsampling statistics used to compute the P value depends on the sample size and the length of the break period. Simulations show that kernel smoothed P values provide more accurate tests in small samples.
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- Dufour, J.M. & Ghysels, E. & Hall, A., 1992.
"Generalized Predictive Tests and Structural Change Analysis in Econometrics,"
Cahiers de recherche
9223, Universite de Montreal, Departement de sciences economiques.
- Dufour, Jean-Marie & Ghysels, Eric & Hall, Alastair, 1994. "Generalized Predictive Tests and Structural Change Analysis in Econometrics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(1), pages 199-229, February.
- Dufour, J.M. & Ghysels, E. & Hall, A., 1992. "Generalized Predictive Tests and Structural Change Analysis in Econometrics," Cahiers de recherche 9223, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Jeff Racine & James G. MacKinnon, 2006.
"Inference via kernel smoothing of bootstrap P values,"
1054, Queen's University, Department of Economics.
- Racine, Jeffrey S. & MacKinnon, James G., 2007. "Inference via kernel smoothing of bootstrap P values," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5949-5957, August.
- D. W. K. Andrews, 2003.
"End-of-Sample Instability Tests,"
Econometric Society, vol. 71(6), pages 1661-1694, November.
- Russell Davidson & James G. MacKinnon, 2001.
"Bootstrap Tests: How Many Bootstraps?,"
1036, Queen's University, Department of Economics.
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