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

Recent trends in the Machine Learning (ML) and in particular Deep Learning (DL) domains
have demonstrated that with the availability of massive amounts of time series, ML and DL …

In time series predictor evaluation, we observe that with respect to the model selection
procedure there is a gap between evaluation of traditional forecasting procedures, on the …

The Diebold–Mariano (DM) test was intended for comparing forecasts; it has been, and
remains, useful in that regard. The DM test was not intended for comparing models. Much of …

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 …

We address some of the key questions that arise in forecasting the price of crude oil. What
do applied forecasters need to know about the choice of sample period and about the …

We explore the implications of a country's external constraint for the dynamics of net foreign
assets, returns, and exchange rates. Deteriorations in external accounts imply future trade …

This chapter discusses recent developments in inflation forecasting. We perform a horse-
race among a large set of traditional and recently developed forecasting methods, and …

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 …

We propose new methods for comparing the out‐of‐sample forecasting performance of two
competing models in the presence of possible instabilities. The main idea is to develop a …