A bayesian approach for predicting with polynomial regresión of unknown degree

Irwin Guttman and María Dolores Redondas
Authors registered in the RePEc Author Service: Daniel Peña

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: This article presents a comparison of four methods to compute the posterior probabilities of the possible orders in polynomial regression models. These posterior probabilities are used for forecasting by using Bayesian model averaging. It is shown that Bayesian model averaging provides a closer relationship between the theoretical coverage of the high density predictive interval (HDPI) and the observed coverage than those corresponding to selecting the best model. The performance of the different procedures are illustrated with simulations and some known engineering data.

Date: 2003-04
New Economics Papers: this item is included in nep-cmp and nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:ws032104

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