Determination of long-run and short-run dynamics in EC-VARMA models via canonical correlations
Data(s) |
01/01/2015
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
John Wiley & Sons |
Relação |
http://dro.deakin.edu.au/eserv/DU:30085227/yao-determinationoflongrun-2016.pdf http://dro.deakin.edu.au/eserv/DU:30085227/yao-determinationoflongrun-inpress-2015.pdf http://www.dx.doi.org/10.1002/jae.2484 |
Direitos |
2015, John Wiley & Sons |
Tipo |
Journal Article |