Forecasting with Shadow-Rate VARs

Forecasting with Dimension Switching VARs

This paper develops methods for Bayesian 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 in forecasting N∗ of them. Thus, the researcher is faced with 2N−N ∗ potential VARs. If N is lar...

Forecasting with Medium and Large Bayesian VARs

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with sm...

Forecasting VARs, Model Selection, and Shrinkage

This paper provides an empirical comparison of various selection and penalized regression approaches for forecasting with vector autoregressive systems. In particular, we investigate the effect of the system size as well as the effect of various prior specification choices on the relative and overall forecasting performance of the methods. The data set is a typical macroeconomic quarterly data ...

Forecasting with Small Macroeconomic VARs in the Presence of Instabilities

Small–scale VARs are widely used in macroeconomics for forecasting U.S. output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time–varying parameters, ...

Rejoinder to Comments on Forecasting Economic and Financial Variables with Global VARs