Specifying vector autoregressions for macroeconomic forecasting

Robert Litterman

No 92, Staff Report from Federal Reserve Bank of Minneapolis

Abstract: This paper describes a Bayesian specification procedure used to generate a vector autoregressive model for forecasting macroeconomic variables. The specification search is over parameters of a prior. This quasi-Bayesian approach is viewed as a flexible tool for constructing a filter which optimally extracts information about the future from a set of macroeconomic data. The procedure is applied to a set of data and a consistent improvement in forecasting performance is documented.

Date: 1984
New Economics Papers: this item is included in nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
https://www.minneapolisfed.org/research/sr/sr92.pdf Full Text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fip:fedmsr:92

Access Statistics for this paper

More papers in Staff Report from Federal Reserve Bank of Minneapolis Contact information at EDIRC.
Bibliographic data for series maintained by Kate Hansel ().