Forecasting in large cointegrated processes

Hiroaki Chigira, Taku Yamamoto

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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 fi nd 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.

Original languageEnglish
Pages (from-to)631-650
Number of pages20
JournalJournal of Forecasting
Volume28
Issue number7
DOIs
Publication statusPublished - 2009 Nov 1

Keywords

  • Cointegration
  • Forecasting
  • Large models

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research

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