Forcasting in large cointegrated processes
It is widely recognized that taking cointegration relationships into consideration is useful in forecasting cointegrated processes. However, there are a few practical problems when forecasting large cointegrated processes using the well-known vector error correction model. First, it is hard to identify the cointegration rank in large models. Second, since the number of parameters to be estimated tends to be large relative to the sample size in large models, estimators will have large standard errors, and so will forecasts. The purpose of the present paper is to propose a new procedure for forecasting large cointegrated processes, which is free from the above problems. In our Monte Carlo experiment, we find that our forecast gains accuracy when we work with a larger model as long as the ratio of the cointegration rank to the number of variables in the process is high.
|Date of creation:||Jun 2006|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.ier.hit-u.ac.jp/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:hst:hstdps:d06-169. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Tatsuji Makino)
If references are entirely missing, you can add them using this form.