.png){kind=icon}
.png){kind=icon}
Return
Estimating and testing high dimensional factor models with multiple structural changes
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2020.04.005.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper considers multiple changes in the factor loadings of a high dimensional factor model occurring at dates that are unknown but common to all subjects. Since the factors are unobservable, the problem is converted to estimating and testing structural changes in the second moments of the pseudo factors. We consider both joint and sequential estimation of the change points and show that the distance between the estimated and the true change points is O-p(1). We find that the estimation error contained in the estimated pseudo factors has no effect on the asymptotic properties of the estimated change points as the cross-sectional dimension N and the time dimension T go to infinity jointly. No N -T ratio condition is needed. We also propose (i) tests for no change versus l changes (ii) tests for l changes versus l + 1 changes, and show that using estimated root factors asymptotically has no effect on their limit distributions if root T/N -> 0. These tests allow us to make inference on the presence and number of structural changes. Simulation results show good performance of the proposed procedure. In an application to US quarterly macroeconomic data we detect two possible breaks. (c) 2020 Elsevier B.V. All rights reserved.
Keywords:
Factor model
Multiple changes
Model selection
Panel data
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
5.2K
Citations:
3.0W
Organization
View more organizations