We propose methods for evaluating density forecasts. We focus primarily on methodsquot; that are applicable regardless of the particular user's loss function. We illustrate the methodsquot; with a detailed simulation example, and then we present an application to density forecasting ofquot;...

We consider the problem of short-term time series forecasting (nowcasting) when there are more possible predictors than observations. Our approach combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging. We illustrate this approach using search engine...

We consider both frequentist and empirical Bayes forecasts of a single time series using a linear model with T observations and K orthonormal predictors. The frequentist formulation considers estimators that are equivariant under permutations (reorderings) of the regressors. The empirical Bayes...

Recent work has analyzed the forecasting performance of standard dynamic stochastic general equilibrium (DSGE) models, but little attention has been given to DSGE models that incorporate nonlinearities in exogenous driving processes. Against that background, we explore whether incorporating...

We use data from the Survey of Professional Forecasters to compare point forecasts of GDP growth and inflation with the subjective probability distributions held by forecasters. We find that SPF forecasters summarize their underlying distributions in different ways and that their summaries tend...

We develop a nonlinear state-space model that captures the joint dynamics of consumption, dividend growth, and asset returns. Our model consists of an economy containing a common predictable component for consumption and dividend growth and multiple stochastic volatility processes. The...

This paper develops and illustrates a simple method to generate a DSGE model-based forecast for variables that do not explicitly appear in the model (non-core variables). We use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to...

Strategies selected by combining multiple signals suffer severe overfitting biases, because underlying signals are typically signed such that each predicts positive in-sample returns. “Highly significant” backtested performance is easy to generate by selecting stocks on the basis of...