Detecting Big Structural Breaks in Large Factor Models

Liang Chen, Juan Dolado and Jesus Gonzalo

No 677, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: Time invariance of factor loadings is a standard assumption in the analysis of large factor models. Yet, this assumption may be restrictive unless parameter shifts are mild (i.e., local to zero). In this paper we develop a new testing procedure to detect big breaks in these loadings at either known or unknown dates. It relies upon testing for parameter breaks in a regression of one of the factors estimated by Principal Components analysis on the remaining estimated factors, where the number of factors is chosen according to Bai and Ng's (2002) information criteria. The test fares well in terms of power relative to other recently proposed tests on this issue, and can be easily implemented to avoid forecasting failures in standard factor-augmented (FAR, FAVAR) models where the number of factors is a priori imposed on the basis of theoretical considerations.

Keywords: Structural break; large factor model; factor loadings; principal components (search for similar items in EconPapers)
JEL-codes: C12 C33 (search for similar items in EconPapers)
Date: 2013-10-14
New Economics Papers: this item is included in nep-ets
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Related works:
Journal Article: Detecting big structural breaks in large factor models (2014)
Working Paper: Detecting big structural breaks in large factor models (2011)
Working Paper: Detecting big structural breaks in large factor models (2011)
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