A Generalized Factor Model with Local Factors (Job Market Paper)
I extend the theory on factor models by incorporating “local” factors into the model. Local factors affect a decreasing fraction of the observed variables. This implies a continuum of eigenvalues of the covariance matrix, as is commonly observed in applications. I derive conditions under which local factors will be estimated consistently using the common Principal Component Estimator. I further propose a novel class of estimators for the number of factors. Unlike estimators that have been proposed in the past, my estimators use information in the eigenvectors as well as in the eigenvalues. Monte Carlo evidence suggests significant finite sample gains over existing estimators. Empirically I find evidence of local factors in a large panel of macroeconomic indicators in the US.
Pre-event Trends in the Panel Event-study Design (Joint with Christian Hansen and Jesse M. Shapiro)
We consider a linear panel event-study design in which unobserved confounds may be related both to the outcome and to the policy variable of interest. We provide sufficient conditions for identification exploiting covariates related to the policy variable only through the unobserved counfounds. Our model implies a set of moment equations that are linear in parameters. The effect of the policy can be estimated by 2SLS, and causal inference is valid even when endogeneity leads to pre-event trends (“pre-trends”) in the outcome. Alternative approaches, such as estimation following a test for pre-trends, perform poorly.
Work in Progress
Sparse Factor Models
Time Varying Correlation Matrices: The Role of Dormant Factors