Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data

• Tác giả: Jushan Bai, Serena Ng
• Ngôn ngữ: eng
• Ký hiệu phân loại: 512.5 Linear algebra
• Thông tin xuất bản: 2019
• Bộ sưu tập: Báo, Tạp chí
• ID: 163483

Tóm tắt

This paper proposes an imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of data. Assuming that a strong factor structure holds for the full panel of data and its sub-blocks, it is shown that the common component can be consistently estimated at four different rates of convergence without requiring regularization or iteration. An asymptotic analysis of the estimation error is obtained. An application of our analysis is estimation of counterfactuals when potential outcomes have a factor structure. We study the estimation of average and individual treatment effects on the treated and establish a normal distribution theory that can be useful for hypothesis testing.