Hostname: page-component-848d4c4894-hfldf Total loading time: 0 Render date: 2024-06-06T00:48:27.074Z Has data issue: false hasContentIssue false

CRITICAL VALUES AND P VALUES OF BESSEL PROCESS DISTRIBUTIONS: COMPUTATION AND APPLICATION TO STRUCTURAL BREAK TESTS

Published online by Cambridge University Press:  24 September 2003

Arturo Estrella
Affiliation:
Federal Reserve Bank of New York

Abstract

The p values of structural break tests, when the break date or dates are unknown, must be calculated in terms of the probability distributions of functions of Bessel processes. The literature so far has maintained that direct computation of these p values and of the corresponding critical values is too difficult and has relied on approximations based on simulations, asymptotic expansions, or curve fitting. This paper presents a fast simple method of calculating exact p values and critical values and uses the method to evaluate the accuracy of the various approximations.The author is grateful for comments and suggestions from Don Andrews, Clint Cummins, Jeff Fuhrer, Ken Garbade, Jim Mahoney, Tony Rodrigues, Josh Rosenberg, Sebastian Schich, participants in a workshop at the Federal Reserve Bank of New York, and the referees. The views expressed in this paper are those of the author and do not necessarily represent those of the Federal Reserve Bank of New York or the Federal Reserve System.

Type
MISCELLANEA
Copyright
© 2003 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Abramowitz, M. & I.A. Stegun (1964) Handbook of Mathematical Functions. Washington, D.C.: U.S. Department of Commerce.
Akman, V.E. & A.E. Raftery (1986) Asymptotic inference for a change-point Poisson process. Annals of Statistics 14, 15831590.Google Scholar
Anderson, T.W. & D.A. Darling (1952) Asymptotic theory of certain “goodness of fit” criteria based on stochastic processes. Annals of Mathematical Statistics 23, 193212.Google Scholar
Andrews, D.W.K. (1993) Tests for parameter instability and structural change with unknown change point. Econometrica 61, 821856.Google Scholar
Andrews, D.W.K. (2003) Tests for parameter instability and structural change with unknown change point: Corrigendum. Econometrica 71, 395397.Google Scholar
Andrews, D.W.K. & R.C. Fair (1988) Inference in nonlinear econometric models with structural change. Review of Economic Studies 55, 615640.Google Scholar
Andrews, D.W.K. & W. Ploberger (1984) Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 62, 13831414.Google Scholar
Bai, J. (1999) Likelihood ratio tests for multiple structural changes. Journal of Econometrics 91, 299323.Google Scholar
Bai, J. & P. Perron (1998) Estimating and testing linear models with multiple structural changes. Econometrica 66, 4778.Google Scholar
DeLong, D.M. (1981) Crossing probabilities for a square root boundary by a Bessel process. Communications in Statistics—Theory and Methods A10, 21972213.Google Scholar
Dirkse, J.P. (1975) An absorption probability for the Ornstein–Uhlenbeck process. Journal of Applied Probability 12, 595599.Google Scholar
Estrella, A. & J.C. Fuhrer (2003) Monetary policy shifts and the stability of monetary policy models. Review of Economics and Statistics 85, 94104.Google Scholar
Ghysels, E., A. Guay, & A. Hall (1997) Predictive tests for structural change with unknown breakpoint. Journal of Econometrics 82, 209233.Google Scholar
Hansen, B.E. (1997) Approximate asymptotic P values for structural-change tests. Journal of Business and Economic Statistics 15, 6067.Google Scholar
James, B., K.L. James, & D. Siegmund (1987) Tests for a change-point. Biometrika 74, 7183.Google Scholar
Miller, R. & D. Siegmund (1982) Maximally selected chi square statistics. Biometrics 38, 10111016.Google Scholar
Siegmund, D. (1985) Sequential Analysis: Tests and Confidence Intervals. New York: Springer-Verlag.
Siegmund, D. (1986) Boundary crossing probabilities and statistical applications. Annals of Statistics 14, 361404.Google Scholar