Hostname: page-component-77f85d65b8-6c7dr Total loading time: 0 Render date: 2026-04-20T08:35:55.081Z Has data issue: false hasContentIssue false
  • English
  • Français

TESTING FOR STRUCTURAL CHANGE BY ISOTONIC REGRESSION

Published online by Cambridge University Press:  21 July 2025

Bin Chen  and
Robert de Jong
Bin Chen*
Affiliation:
University of Rochester
Robert de Jong
Affiliation:
Ohio State University
*
Address correspondence to Bin Chen, Department of Economics, University of Rochester, Rochester, New York, NY, USA, e-mail: binchen@rochester.edu
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Detecting structural changes in economic relationships has been a longstanding challenge in econometrics. Most of the literature on structural breaks has considered abrupt structural breaks. Existing tests for detecting smooth structural change typically rely on kernel estimation. In this article, we introduce a novel tuning-parameter-free test that minimizes a criterion function over all possible nondecreasing or nonincreasing structural change functions. This test is pivotal (after appropriate scaling) in the scalar case and remains computationally simple even in multivariate settings. Compared to existing nonparametric tests, our method offers superior power against local monotonic structural changes and does not involve the choice of a bandwidth parameter. A simulation study and two empirical examples highlight the merits of the proposed test relative to some popular tests for structural changes in the literature.

Information

Type
ARTICLES
Information
Econometric Theory , First View , pp. 1 - 32
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Footnotes

We thank the editor, Peter C. B. Phillips, the co-editor, Anna Mikusheva, and two referees for careful and constructive comments. Any remaining errors are solely ours. The second author gratefully acknowledges financial support from the Jubiläumsfonds of the Austrian Central Bank (Grant No. 15334).

References

REFERENCES

Alogoskoufis, S., & Smith, R. (1991). The Phillips curve, the persistence of inflation, and the Lucas critique: Evidence from exchange rate regimes. American Economic Review , 81, 1254–1275.Google Scholar
Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica , 61, 821–856.10.2307/2951764CrossRefGoogle Scholar
Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica , 66, 47–78.10.2307/2998540CrossRefGoogle Scholar
Bai, J., & Perron, P. (2003), Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18, 1–22.10.1002/jae.659CrossRefGoogle Scholar
Bernanke, B., & Blanchard, O. (2024). What caused the U.S. pandemic-era inflation?. American Economic Journal: Macroeconomics , forthcoming. Google Scholar
Chen, B., & Hong, Y. (2012). Testing for smooth structural changes in time series models via nonparametric regression. Econometrica , 80, 1157–1183.Google Scholar
Chow, G. C. (1960). Tests of equality between subsets of coefficients in two linear regressions. Econometrica , 28, 591–605.10.2307/1910133CrossRefGoogle Scholar
Delworth, T. L., & Knutson, T. R. (2000). Simulation of early 20th century global warming. Science , 287, 2246–2250.10.1126/science.287.5461.2246CrossRefGoogle ScholarPubMed
Elliott, G., & Müller, U. (2006). Efficient tests for general persistent time variation in regression coefficients. Review of Economic Studies , 73, 907–940.10.1111/j.1467-937X.2006.00402.xCrossRefGoogle Scholar
Hansen, B. (2001). The new econometrics of structural change: Dating breaks in U.S. labor productivity. Journal of Economic Perspectives , 15, 117–128.10.1257/jep.15.4.117CrossRefGoogle Scholar
Jennrich, R. I. (1969). Asymptotic properties of non-linear least squares estimators. The Annals of Mathematical Statistics , 40, 633–643.10.1214/aoms/1177697731CrossRefGoogle Scholar
Kristensen, D. (2012). Nonparametric detection and estimation of structural change. Econometrics Journal , 15, 420–461.10.1111/j.1368-423X.2012.00378.xCrossRefGoogle Scholar
Li, D., Phillips, P. C. B., & Gao, J. (2020). Kernel-based inference in time-varying coefficient cointegrating regression. Journal of Econometrics , 215, 607–632.10.1016/j.jeconom.2019.10.005CrossRefGoogle Scholar
Lin, C. J., & Teräsvirta, T. (1994). Testing the constancy of regression parameters against continuous structural change. Journal of Econometrics , 62, 211–228.10.1016/0304-4076(94)90022-1CrossRefGoogle Scholar
Melillo, J. M. (1999). Climate change: Warm, warm on the range. Science , 283, 183–184.10.1126/science.283.5399.183CrossRefGoogle Scholar
Nordhaus, W. (2019). Climate change: The ultimate challenge for economics. American Economic Review , 109(6), 1991–2014.10.1257/aer.109.6.1991CrossRefGoogle Scholar
Robertson, T., Wright, F. T., & Dykstra, R. L. (1988). Order restricted statistical inference . John Wiley.Google Scholar
Su, L., & Wang, X. (2017). On time-varying factor models: Estimation and testing. Journal of Econometrics , 198, 84–101.10.1016/j.jeconom.2016.12.004CrossRefGoogle Scholar
Whittington, L. A., Alm, J., & Peters, H. E. (1990). Fertility and the personal exemption: Implicit pronatalist policy in the United States. American Economic Review , 80, 545–556.Google ScholarPubMed
Wooldridge, J. (2008). Introductory econometrics: A modern approach . Cengage Learning.Google Scholar
Wu, W. B., Woodroofe, M., & Mentz, G. (2001). Isotonic regression: Another look at the changepoint problem. Biometrika , 88, 793–804.10.1093/biomet/88.3.793CrossRefGoogle Scholar
Zhang, T., & Wu, W. B. (2012). Inference of time-varying regression models. Annals of Statistics , 40, 1376–1402.10.1214/12-AOS1010CrossRefGoogle Scholar