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Revista Colombiana de Estadística
Print version ISSN 0120-1751
Rev.Colomb.Estad. vol.44 no.2 Bogotá July/Dec. 2021 Epub Sep 01, 2021
https://doi.org/10.15446/rce.v44n2.91356
Original articles of research
Forecasting with Multivariate Threshold Autoregressive Models
Pronósticos con modelos multivariados autorregresivos de umbrales
1 Statistics Department, Faculty of Science, Universidad Nacional de Colombia city, Colombia
An important stage in the analysis of time series is forecasting of the interest variables. However, the forecasting in non-linear time series models is not straightforward as in linear time series models because an exact analytical expression for the conditional expectation it is not easy to obtain. In this paper, a procedure for forecasting with multivariate threshold autoregressive(MTAR) models is proposed via the so-called predictive distributions in the Bayesian approach. This strategy gives us the forecasts for the response and exogenous variable vectors. The coverage percentages of the forecast intervals and the variability of the predictive distributions are analyzed in this work. An application in the Hydrology field is presented.
Key words: Bayesian approach; Forecasting; Predictive distributions; Coverage percentages; Multivariate threshold autoregressive Model
Una etapa importante en el análisis de series de tiempo es el pronóstico de las variables de interés. 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 analítica exacta de la esperanza condicional no es fácil. En este artículo, un procedimiento de pronóstico con modelos multivariados autorregresivos de umbrales(MTAR) es propuesta vía las llamadas distribuciones predictivas en el enfoque Bayesiano. Esta estrategia nos entrega tanto los pronósticos del vector de respuesta, como el de las variables exógenas. Los porcentajes de cobertura de los intervalos de pronósticos y la variabilidad de las distribuciones predictivas son analizadas en este trabajo. Una aplicación al campo de la hidrología es presentada.
Palabras clave: Enfoque bayesiano; Pronóstico; Distribuciones predictivas; Porcentajes de cobertura; Modelo multivariado Autoregresivo de umbrales
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Received: November 2020; Accepted: May 2021
This is an open-access article distributed under the terms of the Creative Commons Attribution License
