Determination of long-run and short-run dynamics in EC-VARMA models via canonical correlations

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


This article studies a simple, coherent approach for identifying and estimating error-correcting vector autoregressive moving average (EC-VARMA) models. Canonical correlation analysis is implemented for both determining the cointegrating rank, using a strongly consistent method, and identifying the short-run VARMA dynamics, using the scalar component methodology. Finite-sample performance is evaluated via Monte Carlo simulations and the approach is applied to modelling and forecasting US interest rates. The results reveal that EC-VARMA models generate significantly more accurate out-of-sample forecasts than vector error correction models (VECMs), especially for short horizons. 
Original languageEnglish
Pages (from-to)1100-1119
Number of pages20
JournalJournal of Applied Econometrics
Issue number6
Publication statusPublished - 1 Sep 2016

Cite this