SMU SOE Seminar (Apr 5, 2019): Spurious Factor Analysis

Please click here if you are unable to view this page.

ABSTRACT This paper draws parallels between the Principal Components Analysis of factorless high-dimensional nonstationary data and the classical spurious regression. We show that a few of the princpal components of such data absorbs nearly all the data variation. The corresponding scree plot suggests that the data contains a few factors, which is collaborated by the standard panel information criteria. Furthermore, the Dickey-Fuller tests of the unit root hypothesis applied to the estimated “idiosyncratic terms” often reject, creating an impression that a few factors are responsible for most of the non-stationarity in the data. We warn empirical researchers of these peculiar effects and suggest to always compare the analysis in levels with that in differences. Keywords: Spurious regression, Principal components, Factor models, Karhunen-Loève expansion. Click here to view the CV.
		PRESENTER Alexey Onatskiy University of Cambridge
		RESEARCH FIELDS Econometrics Statistics Factor Models Large Random Matrices
		DATE: 5 April 2019 (Friday)
		TIME: 4pm - 5.30pm
		VENUE: Meeting Room 5.1, Level 5 School of Economics Singapore Management University 90 Stamford Road Singapore 178903