We use the adaptive LASSO from the statistical learning literature to identify economically connected industries in a general predictive regression framework. The framework permits complex industry interdependencies, including both direct and indirect sectoral links. Consistent with gradual information diffusion across economically connected industries, we find extensive evidence that lagged re...

This paper examines return predictability when the investor is uncertain about the right state variables. A novel feature of the model averaging approach used in this paper is to account for finite-sample bias of the coefficients in the predictive regressions. Drawing on an extensive international dataset, we find that interest-rate related variables are usually among the most prominent predict...

Empirical studies have suggested that stock returns can be predicted by financial variables such as the dividend-price ratio. However, these studies typically ignore the high persistence of predictor variables, which can make first-order asymptotics a poor approximation in finite samples. Using a more accurate asymptotic approximation, we propose two methods to deal with the persistence problem...

We study an investor’s optimal consumption and portfolio choice problem when he confronts with two possibly misspecified submodels of stock returns: one with IID returns and the other with predictability. We adopt a generalized recursive ambiguity model to accommodate the investor’s aversion to model uncertainty. The investor deals with specification doubts by slanting his beliefs about submode...