Multi-Unknown Breaks Estimates in Presence of Fractional White Noise Disturbance

Abstract:

This paper analyzes the distribution of breaks estimators and finite sample property of the method based on different mean break and long memory situations based on Bai and Perron (1998) multi-unknown breaks method. As to the distribution of the multiple break estimates, the property of the estimates under the alternative hypothesis is better when d is close to-0.5 that the bias and variance are smaller and the distribution are smoother even better than the BP (1998) case. Further work can be made on the limit distribution of break number test statistics in the case of d (-0.5, 0), d (0, 0.5) and to the case of three and more mean, trend breaks.

• Periodical: Applied Mechanics and Materials (Volumes 385-386)
• Pages: 1480-1483
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.385-386.1480
• Online since: August 2013
• Authors: Deng Lu
• Keywords: Distribution, Fractional White Noise, Multi-Unknown Breaks Estimates, Property

References

[1] Bai J., Least squares estimation of a shift in linear processes . Journal of Time Series Analysis, 1994, Vol. 15, 453-472.

DOI: 10.1111/j.1467-9892.1994.tb00204.x

[2] Bai J. and Perron P., Estiamting and testing linear models with multiple structural changes. Econometrica, 1998, Vol. 66, 47-78.

DOI: 10.2307/2998540

[3] Bai J and Perron P., Computation and analysis of multiples structural change models , Journal of Applied Econometrics, 2003, Vol. 18, 1-22.

[4] Lavielle M and Moulines E., Least-squares estimation of an unknown number of shifts in a time series , Journal of Time Series Analysis, 2000, Vol. 21, 33-59.

DOI: 10.1111/1467-9892.00172

[5] Lazarova S., Testing for structural change in regression with long memory processes , Journal of Econometrics, 2004. Vol. 129, 329-372.

[6] Liu J and Wu S and Zidek J. V., On Segmented Multivariate Regressions , Statistics Since, 1997, Vol. 7, 497-525.