Detecting big structural breaks in large factor models

Chen, Liang and Dolado, Juan Jose and Gonzalo, Jesus (2011): Detecting big structural breaks in large factor models.

Abstract

Constant factor loadings is a standard assumption in the analysis of large dimensional factor models. Yet, this assumption may be restrictive unless parameter shifts are mild. In this paper we develop a new testing procedure to detect big breaks in factor loadings at either known or unknown dates. It is based upon testing for structural breaks in a regression of the first of the ¯r factors estimated by PC for the whole sample on the remaining r−1 factors, where r is chosen using Bai and Ng´s (2002) information criteria. We argue that this test is more powerful than other tests available in the literature on this issue.

• Item Type: MPRA Paper
• Language: English
• Keywords: structural break; large factor model
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models

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