Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations
研究了一种识别和估计误差修正向量自回归移动平均模型的方法，利用典型相关分析确定协整秩和短期动态，模拟和实证表明该模型比传统VECM预测更准，尤其短期。
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.