SMU SOE Seminar (Mar 21, 2019): Quantile Factor Models

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ABSTRACT In contrast to Approximate Factor Models (AFM), our proposed Quantile Factor Models (QFM) allow for unobserved common factors shifting some parts of the distribution other than the means of observed variables in large panel datasets. When such extra factors exist, the standard estimation tools for AFM fail to extract them and their quantile factor loadings. We propose an estimation procedure to estimate the factors and their factor loadings in a QFM, and study the asymptotic properties of the proposed estimators. We also propose a method that consistently estimates the number of quantile factors. Simulation results confirm that our QFM estimation methods perform reasonably well in finite samples, while four empirical applications provide evidence that extra factors shifting quantiles could be relevant in practice. Click here to view the CV.
		PRESENTER Liang Chen Shanghai University of Finance and Economics
		RESEARCH FIELDS Econometric Theory Applied Econometrics
		DATE: 21 March 2019 (Thursday)
		TIME: 4pm - 5.30pm
		VENUE: Interactive Media Room, Level 5 School of Economics Singapore Management University 90 Stamford Road Singapore 178903