The forecasting literature has identi…fied two important, broad issues. The fi…rst stylized fact is that the predictive … understand what we have learned about forecasting in the presence of instabilities, especially regarding the two questions above …-of-sample forecasting ability, what should researchers do? If there are statistically significant instabilities in the Granger …

The forecasting literature has identi ed two important issues: (i) several predictors have substantial and … learned about forecasting in the presence of instabilities. The empirical evidence raises a multitude of questions. If in …-sample tests provide poor guidance to out-of-sample forecasting ability, what should researchers do? If there are statistically …

This review provides an overview of forecasting methods that can help researchers forecast in the presence of non …

-of-sample than random walk forecasts or Bayesian VAR forecasts. Del Negro and Schorfheide(2013) in particular suggest that the DSGE … model forecast should become the benchmark for forecasting horse races. We compare the real-time forecasting accuracy of the … forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically …

While forecasting is a common practice in academia, government and business alike, practitioners are often left … wondering how to choose the sample for estimating forecasting models. When we forecast inflation in 2014, for example, should we … time series, and the forecasting performance is often quite sensitive to the choice of such window size. In this paper, we …