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 find that the factor strength at which the principal component estimator gives consistent factor estimates coincides with the factor strength at which factors are economically important in several economic models. I further propose a novel class of estimators for the number of those 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. In an empirical application, I find evidence of local factors in a large panel of US macroeconomic indicators.
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 to identify the causal effect of the policy by exploiting covariates related to the policy only through the confounds. 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