Publication Type

Journal Article

Publication Date

12-2016

Abstract

In this paper, we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso. We show that with probability tending to one, our method can correctly determine the unknown number of breaks, and the estimated break dates are sufficiently close to the true break dates. We obtain estimates of the regression coefficients via post Lasso and establish the asymptotic distributions of the estimates of both break ratios and regression coefficients. We also propose and validate a data-driven method to determine the tuning parameter. Monte Carlo simulations demonstrate that the proposed method works well in finite samples. We illustrate the use of our method with a predictive regression of the equity premium on fundamental information.

Discipline

Econometrics

Research Areas

Econometrics

Publication

Econometric Theory

Volume

32

Issue

6

First Page

1376

Last Page

1433

ISSN

0266-4666

Identifier

10.1017/S0266466615000237

Publisher

Cambridge University Press (CUP): HSS Journals

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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Econometrics Commons

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