This chapter provides an overview of pseudo-out-of-sample tests of unconditional predictive ability. We begin by providing an overview of the literature, including both empirical applications and theoretical contributions. We then delineate two distinct methodologies for conducting inference:...

From Bayesian estimates of a vector autoregression that allows for both coefficient drift and stochastic volatility, we obtain the following three results. First, beginning in approximately 1975, the responsiveness of core inflation to changes in energy prices in the United States fell rapidly...

Sharp rises in energy and other commodity prices have recently ignited concerns about inflation. Will these price increases spill over to other prices more generally? We study the typical responses of different price shocks and assess whether the recent behavior of producer and consumer prices...

This article develops a simple bootstrap method for simulating asymptotic critical values for tests of equal forecast accuracy and encompassing among many nested models. Our method combines elements of fixed regressor and wild bootstraps. We first derive the asymptotic distributions of tests of...

Should the unanticipated slowing of inflation that has occurred since early 2012 raise doubts about the reliability of inflation forecasts? We answer this question by conducting a few exercises with a common macroeconomic forecasting model. Our results indicate that even though inflation turned...

This paper presents evidence linking in-sample tests of predictive content and out-of-sample forecast accuracy. Our approach focuses on the negative effect that finite-sample estimation error has on forecast accuracy despite the presence of significant population-level predictive content. We...

This paper develops a method for producing current-quarter forecasts of GDP growth with a (possibly large) range of available within-the-quarter monthly observations of economic indicators, such as employment and industrial production, and financial indicators, such as stock prices and interest...

The estimation of large vector autoregressions with stochastic volatility using standard methods is computationally very demanding. In this paper we propose to model conditional volatilities as driven by a single common unobserved factor. This is justified by the observation that the pattern of...

This paper compares alternative models of time-varying macroeconomic volatility on the basis of the accuracy of point and density forecasts of macroeconomic variables. In this analysis, we consider both Bayesian autoregressive and Bayesian vector autoregressive models that incorporate some form...