IDEAS home Printed from https://ideas.repec.org/a/bpj/sndecm/v26y2022i2p219-254n8.html
   My bibliography  Save this article

Multiple structural breaks in cointegrating regressions: a model selection approach

Author

Listed:
  • Schmidt Alexander

    (Bright Cape B.V., Heggeranklaan 1, 5643 BP Eindhoven, Netherlands)

  • Schweikert Karsten

    (University of Hohenheim, Core Facility Hohenheim and Institute of Economics, Schloss Hohenheim 1 C, 70593 Stuttgart, Germany)

Abstract

In this paper, we propose a new approach to model structural change in cointegrating regressions using penalized regression techniques. First, we consider a setting with known breakpoint candidates and show that a modified adaptive lasso estimator can consistently estimate structural breaks in the intercept and slope coefficient of a cointegrating regression. Second, we extend our approach to a diverging number of breakpoint candidates and provide simulation evidence that timing and magnitude of structural breaks are consistently estimated. Third, we use the adaptive lasso estimation to design new tests for cointegration in the presence of multiple structural breaks, derive the asymptotic distribution of our test statistics and show that the proposed tests have power against the null of no cointegration. Finally, we use our new methodology to study the effects of structural breaks on the long-run PPP relationship.

Suggested Citation

  • Schmidt Alexander & Schweikert Karsten, 2022. "Multiple structural breaks in cointegrating regressions: a model selection approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 26(2), pages 219-254, April.
    • Handle: RePEc:bpj:sndecm:v:26:y:2022:i:2:p:219-254:n:8
    DOI: 10.1515/snde-2020-0063
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/snde-2020-0063
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/snde-2020-0063?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    adaptive lasso; cointegration; penalized estimation; purchasing power parity; structural breaks; 62E20; 62J07; 91B84;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    Statistics

    Access and download statistics

    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:bpj:sndecm:v:26:y:2022:i:2:p:219-254:n:8. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.