squared error (PMSE) in simulated out-of-sample (SOOS) forecasts. Alternatively, forecast models may be selected using … mimimize the true out-of-sample PMSE, allowing for possible misspecification of the forecast models under consideration. We …

model forecast should become the benchmark for forecasting horse races. We compare the real-time forecasting accuracy of the … forecast benchmark when evaluating DSGE models. Indeed,low-dimensional unrestricted AR and VAR forecasts may forecast more …

wondering how to choose the sample for estimating forecasting models. When we forecast inflation in 2014, for example, should we …

Does economic theory help in forecasting key macroeconomic variables? This article aims to provide some insight into … the question by drawing lessons from the literature. The definition of "economic theory" includes a broad range of … examples of theory-based forecasting that have not proven useful, such as theory-driven variable selection and some popular …

lowers the prediction mean-squared error of forecast models of US consumer price inflation. We study bagging methods for …-of-sample forecasts of inflation based on these bagging methods to that of alternative forecast methods, including factor model forecasts …

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption … of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors serially … uncorrelated at the single-period horizon with increasing variance as the forecast horizon grows. Using analytical results we show …