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Forecasting with Multivariate Threshold Autoregressive Models
Pronósticos con modelos multivariados autorregresivos de umbrales
DOI:
https://doi.org/10.15446/rce.v44n2.91356Keywords:
Bayesian Approach, Forecasting, Predictive distributions, Cov- erage Percentages, Multivariate threshold autoregressive model (en)Enfoque Bayesiano, Pronóstico, Distribciones Predictivas, Porcentajes de Cobertura, Modelo Multivariado Autoregresivo de Umbrales (es)
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An important stage in the analysis of time series is the forecasting. How- ever, the forecasting in non-linear time series models is not straightforward as in linear time series models because an exact analytical of the conditional expectation is not easy to obtain. Therefore, a strategy of forecasting with multivariate threshold autoregressive(MTAR) models is proposed via predictive distributions through Bayesian approach. This strategy gives us the forecast for the response and exogenous vectors. The coverage percentages of the forecast intervals and the variability of the predictive distributions are analysed in this work. An application to Hydrology is presented.
Una etapa importante en el análisis de series de tiempo es el pronóstico. Sin embargo, el pronóstico en modelos de series de tiempo no lineales no es directo como en el caso de modelos lineales de series de tiempo porque obtener la forma exacta de la esperanza condicional no es fácil. Por lo tanto, una estrategia de pronóstico con modelos multivariados autoregresivos de umbrales(MTAR) es propuesta vía las distribuciones predictivas usando el enfoque Bayesiano. Esta esttrategia nos entrega tanto el pronóstico del vector de respuesta, como el de las variables exógenas. Los porcentajes de cobertura de los intervalos de pronóstico y la variabilidad de las distribuciones predictivas son analizadas en este trabajo. Una aplicación a la hidrología es presentada.
References
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