Abstract
A test for panel structural mean change is developed from the CUSUM of the panel processes. Limiting null distribution and consistency of the test are established. The test is shown to have stable finite sample sizes than the existing test of Horvath and Huskova (2012) based on the squared CUSUM. If the mean changes are not cancelled in that their average is away from zero, the proposed test has better power than the existing test. On the other hand, if the mean changes are nearly cancelled, the existing test has better power. The proposed tests are illustrated by a real data set analysis.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bai, J. (1997). Estimation of a change point in multiple regression models. Review of Economics and Statistics, 79, 551–563.
Bai, J. (2010). Common breaks in means and variances for panel data. Journal of Econometrics, 157, 78–92.
Bai, J., Lumsdaine, R. L., & Stock, J. H. (1998). Testing for and dating common breaks in multivariate time sereis. Review of Economic Studies, 65, 395–432.
Cho, S., & Shin, D. W. (2016). An integrated heteroscedastic autoregressive model for forecasting realized volatilities. Journal of the Korean Statistical Society, 45, 371–380.
Emerson, J., & Kao, C. (2001). Testing for structural change of a time trend regression in panel data: Part I. Journal of Propagations in Probability and Statistics, 2, 57–75.
Emerson, J., & Kao, C. (2002). Testing for structural change of a time trend regression in panel data: Part II. Journal of Propagations in Probability and Statistics, 2, 207–250.
Han, A. K., & Park, D. (1989). Testing for structural changes in panel data: Application to a study of US foreign trade in manufacturing goods. Review of Economics and Statistics, 71, 135–142.
Horvath, L., & Huskova, M. (2012). Change-point detection in panel data. Journal of Time Series Analysis, 33, 631–648.
Hwang, E., & Shin, D. W. (2013). A CUSUM test for a long memory heterogeneous autoregressive model. Economics Letters, 121, 379–383.
Hwang, E., & Shin, D. W. (2015). A CUSUMSQ test for structural breaks in error variance for a long memory heterogeneous autoregressive model. Statistics & Probability Letters, 99, 167–176.
Hwang, E., & Shin, D. W. (2016). Estimation of structural mean breaks for long-memory data sets. Statistics, (in press).
Li, F., Tian, Z., Xiao, Y., & Chen, Z. (2015). Variance change-point detection in panel data models. Economics Letters, 126, 140–143.
Phillips, P. C. B., & Solo, V. (1992). Asymptotics for linear processes. Annals of Statistics, 20, 971–1001.
Schwert, G. W. (1989). Tests for unit roots: A Monte Carlo investigation. Journal of Business & Economic Statistics, 7, 147–159.
Shi, Y. (2015). Testing change in volatility using panel data. Economics Letters, 134, 107–110.
Song, H., & Shin, D. W. (2015). Long-memories and mean breaks in realized volatilities. Applied Economics Letters, 22, 1273–1280.
Yun, S., & Shin, D. W. (2015). Forecasting the realized variance of the log-return of Korean won US dollar exchange rate addressing jumps both in stock-trading time and in overnight. Journal of the Korean Statistical Society, 44, 390–402.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Shin, D.W., Hwang, E. A CUSUM test for panel mean change detection. J. Korean Stat. Soc. 46, 70–77 (2017). https://doi.org/10.1016/j.jkss.2016.06.003
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1016/j.jkss.2016.06.003