Advanced SearchSearch Tips
Comparison of Structural Change Tests in Linear Regression Models
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Comparison of Structural Change Tests in Linear Regression Models
Kim, Jae-Hee;
  PDF(new window)
The actual power performance of historical structural change tests are compared under various alternatives. The tests of interest are F, CUSUM, MOSUM, Moving Estimates and empirical distribution function tests with both recursive and ordinary least-squares residuals. Our comparison of the structural tests involves limiting distributions under the hypothesis, the ability to detect the alternative hypotheses under one or double structural change, and smooth change in parameters. Even though no version is uniformly superior to the other, the knowledge about the properties of those tests and connections between these tests can be used in practical structural change tests and in further research on other change tests.
Brownian bridge process;Brownian motion process;change-point model;CUSUM;empirical distribution functional test;MOSUM;moving estimates test;recursive residual;stochastic process;structural change;
 Cited by
Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point, Econometrika, 61, 821-856. crossref(new window)

Andrews, D. W. K. and Ploberger, W. (1994). Optimal tests when a nuisance parameter is present only under the alternative, Econometrica, 62, 1383-1414. crossref(new window)

Aue, A., Horvath, L., Huskova, M. and Kokoszka, P. (2006). Change-point monitoring in linear models, Econometric Journal, 9, 373-403. crossref(new window)

Bai, J. (1996). Testing for parameter constancy n linear regressions: An empirical distribution function approach, Econometrica, 64, 597-622. crossref(new window)

Bauer, P. and Hackl, P. (1978). The use of MOSUMs for quality control, Technometrics, 20, 431-436. crossref(new window)

Bauer, P. and Hackl, P. (1980). An extension of the MOSUM technique for quality control, Technometrics, 22, 1-7. crossref(new window)

Brown, L. R., Durbin, J. and Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time, Journal of Royal Statistical Society B, 37, 149-192.

Carlstein, E. (1988). Nonparametric change point estimation, Annals of Statistics, 16, 188-197. crossref(new window)

Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions, Econometrica, 28, 591-604. crossref(new window)

Chu, C. J., Hornik, K. and Kuan, C. (1995a). MOSUM tests for parameter constancy, Biometrika, 82, 603-617. crossref(new window)

Chu, C. J., Hornik, K. and Kuan, C. (1995b). The moving-estimates test for parameter stability, Econometric Theory, 11, 699-720. Biometrika, 65, 243-251. crossref(new window)

Csorgo, M. and Horvath, L. (1987). Nonparametric Tests for the Change-point Problem, Journal of Statistical Planning and Inference, 17, 1-9. crossref(new window)

Csorgo, M. and Horvath, L. (1997). Limit Theorems in Change-Point Analysis, Wiley, New York.

Dumbgen, L. (1991). The asymptotic behavior of some nonparametric change point estimators, Annals of Statistics, 19, 1471-1495. crossref(new window)

Hansen, B. E. (1992). Tests for parameter instability in regressions with I(1) processes, Journal of Business & Econometric Statistics, 10, 321-335. crossref(new window)

Hansen, B. E. (1997). Approximate asymptotic P values for structural-change tests, Journal of Business & Econometric Statistics, 15, 60-67. crossref(new window)

Horvath, L., Huskova, M., Kokoszka, P. and Steinebach, J. (2004). Monitoring changes in linear models, Journal of Statistical Planning and Inference, 126, 225-251. crossref(new window)

Kim, J. and Hart, J. D. (1998). Tests for change in a mean function when the data are dependent, Journal of Time Series Analysis, 19, 399-424. crossref(new window)

Kim, J. and Hart, J. D. (2011). A change-point estimator using local fourier series, Journal of Nonparametric Statistics, 23, 83-98. crossref(new window)

Kuan, C. M. and Hornik, K. (1995). The generalized fluctuation test: A unifying view, Econometric Reviews, 14, 135-161. crossref(new window)

Loynes, R. M. (1980). The empirical distribution function of residuals from generalized regression, Annals of Statistics, 8, 285-298. crossref(new window)

Newey, W. K. and West, K. D. (1987). A simple, positive semi-definite heteroskedasticity and autocorrelation consistent covariance matrix, Econometrica, 55, 703-708. crossref(new window)

Ploberger, W. and Kramer, W. (1992). The CUSUM test with OLS residuals, Econometrica, 60, 271-285. crossref(new window)

Rice, J. (1984). Bandwidth choice for nonparametric regression, Annals of statistics, 12, 1215-1230. crossref(new window)

Silva, E. G. and Teixeira, A. A. C. (2008). Surveying structural change: Seminal contributions and a bibliometric account, Structural Change and Economic Dynamics, 19, 273-300. crossref(new window)

Wang, J. and Zivot, E. (2000). A bayesian time series model of multiple structural changes in level, trend and variance, Journal of Business & Economic Statistics, 18, 374-386. crossref(new window)

Zeileis, A. (2005). A unified approach to structural change tests based on ML scores, F statistics, and OLS residuals, Econometric Reviews, 23, 445-466.

Zeileis, A., Leisch, L., Hansen, B., Hornik, K. and Kleiber, C. (2007). The strucchange Package, R manual.