A generalized least squares estimation method for VARMA models. (Revised edition).
In this paper a new generalized least squares procedure for estimating VARMA models is proposed. This method differs from existing ones in explicitly considering the stochastic structure of the approximation error that arises when lagged innovations are replaced with lagged residuals obtained from a...
| Autores: | , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 1997 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/64177 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/64177 |
| Access Level: | acceso abierto |
| Palabra clave: | VARMA models estimation Generalized least squares Model specification. Modelos VARMA Residuos minimocuadráticos Método de Doble Regresión. Estadística matemática (Matemáticas) 1209 Estadística |
| Sumario: | In this paper a new generalized least squares procedure for estimating VARMA models is proposed. This method differs from existing ones in explicitly considering the stochastic structure of the approximation error that arises when lagged innovations are replaced with lagged residuals obtained from a long VAR. Simulation results indicate that this method improves the accuracy of estimates with small and moderate sample sizes, and increases the frequency of identifying small nonzero parameters, with respect to both Double Regression and exact maximum likelihood estimation procedures. |
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