Multiple Structural Breaks in Cointegrating Regressions: A Model Selection Approach

49 Pages Posted: 5 Dec 2019

See all articles by Alexander Schmidt

Alexander Schmidt

University of Hohenheim

Karsten Schweikert

University of Hohenheim

Date Written: November 19, 2019

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.

Keywords: Adaptive lasso, Cointegration, Penalized estimation, Purchasing power parity, Structural breaks

JEL Classification: C12, C22, C52

Suggested Citation

Schmidt, Alexander and Schweikert, Karsten, Multiple Structural Breaks in Cointegrating Regressions: A Model Selection Approach (November 19, 2019). Available at SSRN: https://ssrn.com/abstract=3489870 or http://dx.doi.org/10.2139/ssrn.3489870

Alexander Schmidt

University of Hohenheim

Schloss 1C
Stuttgart, 70593
Germany

Karsten Schweikert (Contact Author)

University of Hohenheim ( email )

Schloss 1C
Stuttgart, 70593
Germany

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
18
Abstract Views
170
PlumX Metrics