Forecasting and Interpolation Using Vector Autoregressions with Common Trends

Javier Fernandez-Macho, Andrew Harvey and James H. Stock

Annals of Economics and Statistics, 1987, issue 6-7, 279-287

Abstract: A modification of the vector autoregressive model is to include a stochastic trend component in each equation. It is argued that this formulation will lead to a more parsimonious model than traditional vector autoregressions formulated in terms of levels or differences. Common trends, or factors, may be introduced into the model. This leads to certain of the variables being co-integrated and, as shown in Granger and Engle [1987], the model then has an error correction representation. Estimation of the model can be carried out by casting it in state space form and applying the Kalman filter. This enables estimation to be carried out for a very general situation in which observations may be missing, temporally aggregated or observed at different time intervals. The common trends may also be extracted using smoothing techniques. Missing observations can also be estimated and the model is likely to be useful if this is the main objective.

Date: 1987
References: Add references at CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.jstor.org/stable/20075657 (text/html)

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:adr:anecst:y:1987:i:6-7:p:279-287

Access Statistics for this article

Annals of Economics and Statistics is currently edited by Laurent Linnemer

More articles in Annals of Economics and Statistics from GENES Contact information at EDIRC.
Bibliographic data for series maintained by Secretariat General () and Laurent Linnemer ().