Resumen

CALDERON-VILLANUEVA, SERGIO A.  y  NIETO, FABIO H.. Forecasting with Multivariate Threshold Autoregressive Models. Rev.Colomb.Estad. [online]. 2021, vol.44, n.2, pp.369-383.  Epub 01-Sep-2021. ISSN 0120-1751.  https://doi.org/10.15446/rce.v44n2.91356.

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.

Palabras clave : Bayesian approach; Forecasting; Predictive distributions; Coverage percentages; Multivariate threshold autoregressive Model.

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