Multiple structural breaks in cointegrating regressions: a model selection approach
Schmidt Alexander () and
Schweikert Karsten ()
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Schmidt Alexander: Bright Cape B.V., Heggeranklaan 1, 5643 BPEindhoven, Netherlands
Schweikert Karsten: University of Hohenheim, Core Facility Hohenheim and Institute of Economics, Schloss Hohenheim 1 C, 70593Stuttgart, Germany
Studies in Nonlinear Dynamics & Econometrics, 2022, vol. 26, issue 2, 219-254
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.
Keywords: adaptive lasso; cointegration; penalized estimation; purchasing power parity; structural breaks; C12; C22; C52; 62E20; 62J07; 91B84 (search for similar items in EconPapers)
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