Instrumented Principal Component Analysis (WP) w/ Kelly and Su

with B. Kelly and Y. Su


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.

Pruitt’s Python code

Example Matlab code folder