Instrumented Principal Component Analysis
71 Pages Posted: 9 Jun 2017 Last revised: 28 Dec 2020
Date Written: December 17, 2020
We propose a new approach of latent factor analysis that, in addition to the main panel of interest, introduces other relevant data that serve as instruments for dynamic factor loadings.
The method, called IPCA, provides a parsimonious means of incorporating vast conditioning information into factor model estimates. This improves the efficiency of estimates for the latent factors and their loadings, and helps to ascertain the economic relationships among factors and individuals via the observable instruments. The estimation is fast to calculate and accommodates unbalanced panels. We show consistency and asymptotic normality under general panel data generating processes. We demonstrate the advantages of IPCA in simulated data and in applications to equity asset pricing and international macroeconomics.
Keywords: factor model, principal components, tensor, asymptotic theory, international macroeconomics, dynamic loading
JEL Classification: F44, C55
Suggested Citation: Suggested Citation