Constrained forecasting in autoregressive time series models: A Bayesian analysis

Constrained forecasting in autoregressive time series models: A Bayesian analysis

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Article ID: iaor1994386
Country: Netherlands
Volume: 9
Issue: 1
Start Page Number: 95
End Page Number: 108
Publication Date: Mar 1993
Journal: International Journal of Forecasting
Authors:
Keywords: Bayesian forecasting
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.

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