City Research Online

Estimating and Testing High Dimensional Factor Models With Multiple Structural Changes

Baltagi, B. H., Wang, F. & Kao, C. (2020). Estimating and Testing High Dimensional Factor Models With Multiple Structural Changes. Journal of Econometrics, 220(2), pp. 349-365. doi: 10.1016/j.jeconom.2020.04.005


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 Op(1). We Önd that the estimation error contained in the estimated pseudo factors has no e§ect on the asymptotic properties of the estimated change points as the cross-sectional dimension N and the time dimension T go to inÖnity 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 factors asymptotically has no e§ect on their limit distributions if pT =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.

Publication Type: Article
Additional Information: © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: factor model, multiple changes, model selection, panel data
Subjects: H Social Sciences > HB Economic Theory
Q Science > QA Mathematics
Departments: Bayes Business School > Finance
SWORD Depositor:
[thumbnail of Estimating and testing high dimensional factor models with multiple structural changes.pdf]
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (328kB) | Preview


Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email


Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login