IDEAS home Printed from https://ideas.repec.org/p/cdl/ucsdec/qt58n33447.html
   My bibliography  Save this paper

Optimally Testing General Breaking Processes in Linear Time Series Models

Author

Listed:
  • Elliott, Graham
  • Mueller, Ulrich K.

Abstract

There are a large number of tests for instability or breaks in coefficients in regression models designed for different possible departures from a stable regression. We make two contributions to this literature. First, we provide conditions under which optimal tests are asymptotically equivalent. Our conditions allow for models with many or relatively few breaks, clustered breaks, regularly occurring breaks or smooth transitions to changes in the regression coefficients. Thus we show nothing is gained asymptotically by knowing the exact breaking process. Second, we provide a statistic that is simple to compute, avoids any need for searching over high dimensions when there are many breaks, is valid for a wide range of data generating processes and has high power for many alternative

Suggested Citation

  • Elliott, Graham & Mueller, Ulrich K., 2004. "Optimally Testing General Breaking Processes in Linear Time Series Models," University of California at San Diego, Economics Working Paper Series qt58n33447, Department of Economics, UC San Diego.
    • Handle: RePEc:cdl:ucsdec:qt58n33447
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/58n33447.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Engle, Robert F. & Hendry, David F., 1993. "Testing superexogeneity and invariance in regression models," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 119-139, March.
    2. Richard Clarida & Jordi Galí & Mark Gertler, 2000. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(1), pages 147-180.
    3. Sowell, Fallaw, 1996. "Optimal Tests for Parameter Instability in the Generalized Method of Moments Framework," Econometrica, Econometric Society, vol. 64(5), pages 1085-1107, September.
    4. Andrews, Donald W. K. & Lee, Inpyo & Ploberger, Werner, 1996. "Optimal changepoint tests for normal linear regression," Journal of Econometrics, Elsevier, vol. 70(1), pages 9-38, January.
    5. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    6. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    7. Forchini, G., 2002. "Optimal Similar Tests For Structural Change For The Linear Regression Model," Econometric Theory, Cambridge University Press, vol. 18(4), pages 853-867, August.
    8. Hansen, Bruce E., 2000. "Testing for structural change in conditional models," Journal of Econometrics, Elsevier, vol. 97(1), pages 93-115, July.
    9. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    10. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    11. Shively, Thomas S., 1988. "An analysis of tests for regression coefficient stability," Journal of Econometrics, Elsevier, vol. 39(3), pages 367-386, November.
    12. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    13. Ploberger, Werner & Kramer, Walter, 1992. "The CUSUM Test with OLS Residuals," Econometrica, Econometric Society, vol. 60(2), pages 271-285, March.
    14. Hackl, P & Westlund, A H, 1989. "Statistical Analysis of "Structural Change": An Annotated Bibliography," Empirical Economics, Springer, vol. 14(2), pages 167-192.
    15. Ploberger, Werner & Kramer, Walter & Kontrus, Karl, 1989. "A new test for structural stability in the linear regression model," Journal of Econometrics, Elsevier, vol. 40(2), pages 307-318, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Elliott, Graham & Muller, Ulrich K., 2007. "Confidence sets for the date of a single break in linear time series regressions," Journal of Econometrics, Elsevier, vol. 141(2), pages 1196-1218, December.
    2. Travaglini, Guido, 2007. "The U.S. Dynamic Taylor Rule With Multiple Breaks, 1984-2001," MPRA Paper 3419, University Library of Munich, Germany, revised 15 Jun 2007.
    3. Leandro M. Magnusson & Sophocles Mavroeidis, 2014. "Identification Using Stability Restrictions," Econometrica, Econometric Society, vol. 82(5), pages 1799-1851, September.
    4. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    5. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 207-229, August.
    6. Perron, Pierre, 2020. "L'estimation de modèles avec changements structurels multiples," L'Actualité Economique, Société Canadienne de Science Economique, vol. 96(4), pages 789-837, Décembre.
    7. Somayeh Mardaneh, 2012. "How Do Oil Shocks A¤ect the Structural Stability of Hybrid New Keynesian Phillips Curve?," Discussion Papers in Economics 12/20, Division of Economics, School of Business, University of Leicester.
    8. Paye, Bradley S. & Timmermann, Allan, 2006. "Instability of return prediction models," Journal of Empirical Finance, Elsevier, vol. 13(3), pages 274-315, June.
    9. Oleg Glouchakov, 2006. "Joint change point estimation in regression coeffcients and variances of the errors of a linear model," Working Papers 2006_3, York University, Department of Economics.
    10. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
    11. Pierre Perron & Yohei Yamamoto, 2022. "Structural change tests under heteroskedasticity: Joint estimation versus two‐steps methods," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(3), pages 389-411, May.
    12. Sensier, M. & van Dijk, D.J.C., 2001. "Short-term volatility versus long-term growth: evidence in US macroeconomic time series," Econometric Institute Research Papers EI 2001-11, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    13. Pierre Perron & Yohei Yamamoto, 2016. "On the Usefulness or Lack Thereof of Optimality Criteria for Structural Change Tests," Econometric Reviews, Taylor & Francis Journals, vol. 35(5), pages 782-844, May.
    14. Kuan, Chung-Ming, 1998. "Tests for changes in models with a polynomial trend," Journal of Econometrics, Elsevier, vol. 84(1), pages 75-91, May.
    15. Alaa Abi Morshed & Elena Andreou & Otilia Boldea, 2018. "Structural Break Tests Robust to Regression Misspecification," Econometrics, MDPI, vol. 6(2), pages 1-39, May.
    16. Bajo-Rubio, Oscar & Diaz-Roldan, Carmen & Esteve, Vicente, 2007. "Change of regime and Phillips curve stability: The case of Spain, 1964-2002," Journal of Policy Modeling, Elsevier, vol. 29(3), pages 453-462.
    17. Hong, Yongmiao & Linton, Oliver & McCabe, Brendan & Sun, Jiajing & Wang, Shouyang, 2024. "Kolmogorov–Smirnov type testing for structural breaks: A new adjusted-range based self-normalization approach," Journal of Econometrics, Elsevier, vol. 238(2).
    18. Kurozumi Eiji, 2015. "Testing for Multiple Structural Changes with Non-Homogeneous Regressors," Journal of Time Series Econometrics, De Gruyter, vol. 7(1), pages 1-35, January.
    19. Raffaella Giacomini & Barbara Rossi, 2009. "Detecting and Predicting Forecast Breakdowns," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(2), pages 669-705.
    20. Makram El-Shagi & Sebastian Giesen, 2013. "Testing for Structural Breaks at Unknown Time: A Steeplechase," Computational Economics, Springer;Society for Computational Economics, vol. 41(1), pages 101-123, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cdl:ucsdec:qt58n33447. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/deucsus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.