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Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations
Author(s) -
Athanasopoulos George,
Poskitt Donald S.,
Vahid Farshid,
Yao Wenying
Publication year - 2015
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2484
Subject(s) - autoregressive–moving average model , monte carlo method , autoregressive model , econometrics , scalar (mathematics) , cointegration , rank (graph theory) , moving average , mathematics , sample (material) , canonical correlation , error correction model , statistics , computer science , physics , geometry , combinatorics , thermodynamics
Summary 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. Copyright © 2015 John Wiley & Sons, Ltd.