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Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations

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  • George Athanasopoulos
  • Donald S. Poskitt
  • Farshid Vahid
  • Wenying Yao

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.
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  • George Athanasopoulos & Donald S. Poskitt & Farshid Vahid & Wenying Yao, 2016. "Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1100-1119, September.
    • Handle: RePEc:wly:japmet:v:31:y:2016:i:6:p:1100-1119
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    Cited by:

    1. Ivan Mendieta-Munoz & Mengheng Li, 2019. "The Multivariate Simultaneous Unobserved Compenents Model and Identification via Heteroskedasticity," Working Paper Series, Department of Economics, University of Utah 2019_06, University of Utah, Department of Economics.

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    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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