Volume 9 Issue 1 (April-June 1993)

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Constrained forecasting in autoregressive time series models: A Bayesian analysis

de Alba, E.
Pages 95-108
Abstract

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.

Keywords: Conditional , Predictive , Monte Carlo
FULL TEXT LINK
http://dx.doi.org/10.1016/0169-2070(93)90057-T
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