A0210
Title: Factor analysis for causal inference on large non-stationary panels with endogenous treatment
Authors: Markus Pelger - Stanford University (United States) [presenting]
Ruoxuan Xiong - Emory (United States)
Junting Duan - Stanford (United States)
Abstract: The purpose is to study the imputation and inference for large-dimensional non-stationary panel data with general missing observations. The novel method, within-transform-PCA (wi-PCA), transforms the data under endogenous missingness to remove non-stationarities and heterogeneous mean effects before estimating an approximate latent factor structure with PCA. This within-transformation is equivalent to estimating two-way non-stationary fixed effects separately from the latent factor structure. The approach allows for one of the most general and broadly applicable models for data generation and missing patterns in the factor modeling literature. Entry-wise inferential theory is provided for the values imputed with wi-PCA. The key application of wi-PCA is the estimation of counterfactuals on causal panels, where two-way endogenous treatment effects, time trends, and general latent confounders are allowed. In an empirical study of the liberalization of marijuana, it is shown that wi-PCA yields more accurate estimates of treatment effects and more credible economic conclusions compared to its two special cases of conventional difference-in-differences and PCA.
