This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking...

We consider inference about coefficients on a small number of variables of interest in a linear panel data model with additive unobserved individual and time specific effects and a large number of additional time-varying confounding variables. We allow the number of these additional confounding...

We study a panel data model with general heterogeneous effects, where slopes are allowed to be varying across both individuals and times. The key assumption for dimension reduction is that the heterogeneous slopes can be expressed as a factor structure so that the high-dimensional slope matrix...

It has been well known in financial economics that factor betas depend on observed instruments such as firm specific characteristics and macroeconomic variables, and a key object of interest is the effect of instruments on the factor betas. One of the key features of our model is that we specify...

We study factor models augmented by observed covariates that have explanatory powers on the unknown factors. In financial factor models, the unknown factors can be reasonably well explained by a few observable proxies, such as the Fama-French factors. In diffusion index forecasts, identified...

We develop a test for deciding whether the linear spaces spanned by the factor exposures of a large cross-section of assets toward latent systematic risk factors at two distinct points in time are the same. The test uses a panel of asset returns in local windows around the two time points. The...