This chapter summarizes recent literature on asymptotic inference about forecasts. Both
analytical and simulation based methods are discussed. The emphasis is on techniques …

This chapter discusses estimation, specification testing, and model selection of predictive
density models. In particular, predictive density estimation is briefly discussed, and a variety …

This paper introduces the model confidence set (MCS) and applies it to the selection of
models. A MCS is a set of models that is constructed such that it will contain the best model …

Forecast evaluation often compares a parsimonious null model to a larger model that nests
the null model. Under the null that the parsimonious model generates the data, the larger …

We propose a framework for out‐of‐sample predictive ability testing and forecast selection
designed for use in the realistic situation in which the forecasting model is possibly …

We propose a new test for superior predictive ability. The new test compares favorably to the
reality check (RC) for data snoo**, because it is more powerful and less sensitive to poor …

We examine the asymptotic and finite-sample properties of tests for equal forecast accuracy
and encompassing applied to 1-step ahead forecasts from nested linear models. We first …

This paper presents analytical, Monte Carlo and empirical evidence concerning out-of-
sample tests of Granger causality. The environment is one in which the relative predictive …

It is widely known that significant in-sample evidence of predictability does not guarantee
significant out-of-sample predictability. This is often interpreted as an indication that in …

This article proposes new methodologies for evaluating economic models' out-of-sample
forecasting performance that are robust to the choice of the estimation window size. The …