Publication Type
Working Paper
Version
publishedVersion
Publication Date
8-2014
Abstract
In this paper we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso (least absolute shrinkage and selection operator). 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.
Keywords
Change point, Fused Lasso, Group Lasso, Penalized least squares, Structural change
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
51
Publisher
SMU Economics and Statistics Working Paper Series, No. 06-2014
City or Country
Singapore
Citation
QIAN, Junhui and SU, Liangjun.
Shrinkage Estimation of Regression Models with Multiple Structural Changes. (2014). 1-51.
Available at: https://ink.library.smu.edu.sg/soe_research/1595
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Comments
Published in Econometric Theory https://doi.org/10.1017/S0266466615000237