Shrinkage Estimation of Regression Models with Multiple Structural Changes

Junhui Qian and Liangjun Su ()

No 06-2014, Working Papers from Singapore Management University, School of Economics

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 datadriven 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 (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
Pages: 52 pages
Date: 2014-08
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-sea
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Citations: View citations in EconPapers (11)

Published in SMU Economics and Statistics Working Paper Series

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Related works:
Journal Article: SHRINKAGE ESTIMATION OF REGRESSION MODELS WITH MULTIPLE STRUCTURAL CHANGES (2016)
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