SMU SOE Online Seminar (Nov 23, 2020, 10am-11.30am): Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference

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

TOPIC:

LARGE DIMENSIONAL LATENT FACTOR MODELING WITH MISSING OBSERVATIONS AND APPLICATIONS TO CAUSAL INFERENCE

ABSTRACT This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We propose an easy-to-use all-purpose estimator for a latent factor model by applying principal component analysis to an adjusted covariance matrix estimated from partially observed panel data. We derive the asymptotic distribution for the estimated factors, loadings and the imputed values under an approximate factor model and general missing patterns. The key application is to estimate counterfactual outcomes in causal inference from panel data. The unobserved control group is modeled as missing values, which are inferred from the latent factor model. The inferential theory for the imputed values allows us to test for individual treatment eﬀects at any time under general adoption patterns where the units can be aﬀected by unobserved factors. Keywords: Factor analysis, principal components, synthetic control, causal inference, treatment eﬀect, missing entry, large-dimensional panel data, large N and T, matrix completion. JEL Codes: C14, C38, C55, G12. Click here to view the paper. Click here to view the CV.		This seminar will be held via Zoom. A confirmation email with the Zoom details will be sent to the registered email by 20 November 2020.
		PRESENTER Markus Pelger Stanford University
		RESEARCH FIELDS Econometrics Asset Pricing Machine-Learning
		DATE: 23 November 2020 (Monday)
		TIME: 10.00am - 11.30am

© Copyright 2024 by Singapore Management University. All Rights Reserved.