With the concept of trend inflation now being widely understood to be important to the accuracy of longer-term inflation forecasts, this paper assesses alternative models of trend inflation. Reflecting the models which are common in reduced-form inflation modeling and forecasting, we specify a...

Models based on economic theory have serious problems forecasting exchange rates better than simple univariate driftless random walk models, especially at short horizons. Multivariate time series models suffer from the same problem. In this paper, we propose to forecast exchange rates with a...

This paper develops methods for VAR forecasting when the researcher is uncertain about which variables enter the VAR, and the dimension of the VAR may be changing over time. It considers the case where there are N variables which might potentially enter a VAR and the researcher is interested...

This paper builds a model which has two extensions over a standard VAR. The first of these is stochastic search variable selection, which is an automatic model selection device that allows coefficients in a possibly over-parameterized VAR to be set to zero. The second extension allows for an...

In this paper we explore the forecasting performances of methods based on a pre-selection of monthly indicators from large panels of time series. After a preliminary data reduction step based on different shrinkage techniques, we compare the accuracy of principal components forecasts with that...

In this paper, we focus on the different methods which have been proposed in the literature to date for dealing with mixed-frequency and ragged-edge datasets: bridge equations, mixed-data sampling (MIDAS), and mixed-frequency VAR (MF-VAR) models. We discuss their performances for nowcasting the...

As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual...

This paper investigates the problem of constructing prediction regions for forecast trajectories 1 to H periods into the future—a path forecast. When the null model is only approximative, or completely unavailable, one cannot either derive the usual analytic expressions or resample from the...