This paper considers the problem of forecasting under continuous and discrete structural breaks and proposes weighting … only across regimes. In practice, where information on structural breaks is uncertain, a forecasting procedure based on … experiments and an empirical application to forecasting real GDP using the yield curve across nine industrial economies. …

We consider forecasting with factors, variables and both, modeling in-sample using Autometrics so all principal …-error taxonomy for factor models highlights the impacts of location shifts on forecast-error biases. Forecasting US GDP over 1-, 4 … for nowcasting or short-term forecasting, but their relative performance declines as the forecast horizon increases …

We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally integrated moving … rates illustrates the usefulness of our forecasting procedure. The empirical success of the HAR-RV model can be explained …

In this paper, we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive … dimension can change over time. For instance, we can have a large TVP-VAR as the forecasting model at some points in time, but a … use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting …

process prior. Cumulative Bayes factors show our semiparametric model accurately forecasting market returns. During tranquil …