The Internet Appendix collects the proofs and additional results that support the main text. We show in simulations that our estimators perform well relative to alternative estimators and can be improved even further with an iterative approach. We also confirm that the distribution results,...

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...

Experimentation has become an increasingly prevalent tool for guiding decision-making and policy choices. A common hurdle in designing experiments is the lack of statistical power. In this paper, we study the optimal multi-period experimental design under the constraint that the treatment cannot...

Analyzing observational data from multiple sources can be useful for increasing statistical power to detect a treatment effect; however, practical constraints such as privacy considerations may restrict individual-level information sharing across data sets. This paper develops federated methods...

We propose a new method for estimating latent asset pricing factors that fit the time-series and cross-section of expected returns. Our estimator generalizes Principal Component Analysis (PCA) by including a penalty on the pricing error in expected returns. We show that our estimator strongly...

The Supplemental Appendix to "Factors that Fit the Time Series and Cross-Section of Stock Returns" provides additional tables and figures supporting the main text. Among others it includes robustness results for the large cross-section of all decile portfolios and the extended cross-section with...