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

George Athanasopoulos (), Donald Poskitt, Farshid Vahid and Wenying Yao

No 22/14, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: 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 performances are evaluated via Monte-Carlo simulations and the approach is applied to model and forecast 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.

Keywords: Cointegration; Error correction; Scalar Component Model; Multivariate Time Series. (search for similar items in EconPapers)
JEL-codes: C1 C32 C53 (search for similar items in EconPapers)
Date: 2014
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Related works:
Journal Article: Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations (2016)
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