We develop metrics based on Shapley values for interpreting time-series forecasting models, including “black-box” models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics,...

This paper considers inflation forecasting for a vast panel of countries. We combine the information from common factors driving global inflation as well as country-specific inflation in order to build a set of different models. We also rely on new advances in the Machine Learning literature. We...

We present a novel approach to analyzing stock return predictability that accommodates (i) arbitrary predictor persistence, (ii) panels with common factors, (iii) multiple predictors, (iv) short- and long-horizon analysis, and relies on standard inference from least-squares estimation of a...

This paper provides empirical evidence on predictable shifts in the degree of bond return predictability. Bond returns are predictable in high (low) economic activity (uncertainty) states, which suggests that the expectations hypothesis of the term structure holds periodically. These...