Regularizing Bayesian predictive regressions

Variable Selection in Predictive Regressions

This chapter reviews methods for selecting empirically relevant predictors from a set of N potentially relevant ones for the purpose of forecasting a scalar time series. I first discuss criterion based procedures in the conventional case when N is small relative to the sample size, T . I then turn to the large N case. Regularization and dimension reduction methods are then discussed. Irrespecti...

Bayesian Regressions in Experimental Design

The Stambaugh Bias in Panel Predictive Regressions

This paper analyzes predictive regressions in a panel data setting. The standard …xed e¤ects estimator su¤ers from a small sample bias, which is the analogue of the Stambaugh bias in time-series predictive regressions. Monte Carlo evidence shows that the bias and resulting size distortions can be severe. A new bias-corrected estimator is proposed, which is shown to work well in …nite samples an...

Interpreting Long-Horizon Estimates in Predictive Regressions

This paper analyzes the asymptotic properties of long-horizon estimators under both the null hypothesis and an alternative of predictability. Asymptotically, under the null of no predictability, the long-run estimator is an increasing deterministic function of the short-run estimate and the forecasting horizon. Under the alternative of predictability, the conditional distribution of the long-ru...

Predictive Regressions with Time-Varying Coefficients

We evaluate predictive regressions that explicitly consider the time-variation of coefficients in a comprehensive Bayesian framework. This allows for fast and consistent adjustment of regression coefficients to changes in the underlying economic relationships (e.g., changes in the regulatory environment) as we document explicitly for the coefficient of the dividend yield. For monthly returns of...