Forecasting VARMA processes: VAR models vs. subspace-based state space models
AbstractVAR modelling is a frequent technique in econometrics for assumed linear processes. VAR modelling offers some desirable features such as relatively simple procedures for model specification and the possibility of making a quick and non-iterative maximum likelihood estimation of the system parameters. However, if the process under study follows a finite-order VARMA structure, it cannot be equivalently represented by any finite-order VAR model. On the other hand, a finite-order state space model can represent a finite-order VARMA process exactly, and subspace algorithms allow for a simple specification and quick non-iterative estimates. Given the previous facts, we test in this paper whether subspace-based state space models can provide better forecasts than VAR models when working with VARMA data generating processes. In a simulation study we generate identification samples from different VARMA data generating processes, obtain VAR-based and state-space-based models for each generating process and compare the predictive power of the obtained models. We also conduct a practical comparison (for two cointegrated economic time series) of the predictive power of Johansen restricted-VAR (VEC) models with the predictive power of state space models obtained by the CCA subspace algorithm, including a density forecasting analysis
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 271.
Date of creation: 04 Jul 2006
Date of revision:
Forecasting; time series; subspace models;
Find related papers by JEL classification:
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- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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