We build a simple diagnostic criterion for approximate factor structure in large panel datasets. Given observable factors, the criterion checks whether the errors are weakly cross-sectionally correlated or share at least one unobservable common factor (interactive effects). A general version...

We develop a new approach for evaluating performance across hedge funds. Our approach allows for performance comparisons between models that are misspecified – a common feature given the numerous factors that drive hedge fund returns. The empirical results show that the standard models used in...

We develop a novel approach to jointly examine skill, scale, and value added across individual funds. We find that the value added is (i) positive for the vast majority of funds, and (ii) close to its optimal level after an adjustment period possibly due to investors' learning. We also show that...

The recursive prediction and filtering formulas of the Kalman filter are difficult to implement in nonlinear state space models. For Gaussian linear state space models, or for models with qualitative state variables, the recursive formulas of the filter require the updating of a finite number of...

We study the asymptotic distribution of Tikhonov Regularized estimation of quantile structural effects implied by a nonseparable model. The nonparametric instrumental variable estimator is based on a minimum distance principle. We show that the minimum distance problem without regularization is...

We develop an econometric methodology to infer the path of risk premia from large unbalanced panel of individual stock returns. We estimate the time-varying risk premia implied by conditional linear asset pricing models where the conditioning includes instruments common to all assets and asset...