Article

Vars, Cointegration, and Common Cycle Restrictions

Heather M. Anderson and Farshid Vahid

in The Oxford Handbook of Economic Forecasting

Published in print July 2011 | ISBN: 9780195398649
Published online September 2012 | | DOI: http://dx.doi.org/10.1093/oxfordhb/9780195398649.013.0002

Series: Oxford Handbooks

 Vars, Cointegration, and Common Cycle Restrictions

More Like This

Show all results sharing these subjects:

  • Economics
  • Econometric and Statistical Methods and Methodology: General
  • Financial Markets

GO

Show Summary Details

Preview

This article argues that the vector autoregressive (VAR) models with cointegration and common cycles (or weaker forms of rank restrictions) can be usefully viewed as observable factor models. The factors are linear combinations of lagged levels and lagged differences, and as such, these observable factors have forecasting potential. This potential is illustrated in both a Monte Carlo and empirical setting, and the difficulties in developing “rules of thumb” for forecasting in multivariate systems are demonstrated. The article is organized as follows. Section 2 provides a synopsis of the literature on VARs with common trends, common cycles, and other common features. Section 3 extends the Monte Carlo analysis in Lin and Tsay (1996) to illustrate how model selection and the imposition of short- and long-run restrictions affect forecasts. Section 4 studies the forecasting performance of several reduced-rank multivariate models of an updated version of the Litterman (1986) data set, while Section 5 concludes.

Keywords: vector autoregressive model; factor models; economic forecasting; Monte Carlo analysis; multivariate models

Article.  10991 words. 

Subjects: Economics ; Econometric and Statistical Methods and Methodology: General ; Financial Markets

Full text: subscription required

How to subscribe Recommend to my Librarian

Buy this work at Oxford University Press »

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.