Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7408517 | International Journal of Forecasting | 2014 | 11 Pages |
Abstract
This paper develops methods for VARÂ 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 VARÂ and the researcher is interested in forecasting Nâ of them. Thus, the researcher is faced with 2NâNâ potential VARs. If N is large, conventional Bayesian methods can be infeasible due to the computational burden of dealing with a huge model space. Allowing for the dimension of the VAR to change over time only increases this burden. In light of these considerations, this paper uses computationally practical approximations adapted from the dynamic model averaging literature in order to develop methods for dynamic dimension selection (DDS) in VARs. We then show the benefits of DDS in a macroeconomic forecasting application. In particular, DDS switches between different parsimonious VARs and forecasts appreciably better than various small and large dimensional VARs.
Related Topics
Social Sciences and Humanities
Business, Management and Accounting
Business and International Management
Authors
Gary Koop,