Constrained forecasting in autoregressive time series models: a Bayesian analysis

de Alba, E.

International Journal of Forecasting 9(1): 95-108


ISSN/ISBN: 0169-2070
Accession: 082648100

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A Bayesian approach is used to derive constrained and unconstrained forecasts in an autoregressive time series model. Both are obtained by formulating an Ar (p) model in such a way that it is possible to compute numerically the predictive distribution for any number of forecasts. The types of constraints considered are that a linear combination of the forecasts equals a given value. This kind of restriction is applied to forecasting quarterly values whose sum must be equal to a given annual value. Constrained forecasts are generated by conditioning on the predictive distribution of unconstrained forecasts. The procedures are applied to the Quarterly Gnp of Mexico, to a simulated series from an Ar (4) process and to the Quarterly Unemployment Rate for the United States.