Bayesian multiperiod forecasts for ARX models

Autor:	Shu-Ing Liu
Rok vydání:	1995
Předmět:	Statistics and Probability Wishart distribution Set (abstract data type) Autoregressive model Sample size determination Bayesian probability Posterior probability Econometrics Applied mathematics Estimator Time series Mathematics
Zdroj:	Annals of the Institute of Statistical Mathematics. 47:211-224
ISSN:	1572-9052 0020-3157
DOI:	10.1007/bf00773458
Popis:	Bayestian muliperiod forecasts for AR models with random independent exogenous variables under normal-gamma and normal-inverted Wishart prior assumptions are investigated. By suitably arranging the integration order of the model's parameters, at-density mixture approximation is analytically derived to provide an estimator of the posterior predictive density for any future observation. In particular, a suitablet-density is proposed by a convenient closed form. The precision of the discussed methods is examined by using some simulated data and one set of real data up to lead-six-ahead forecasts. It is found that the numerical results of the discussed methods are rather close. In particular, when sample sizes are sufficiently large, it is encouraging to apply a convenientt-density in practical usage. In fact, thist-density estimator asymptotically converges to the true density.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::16bd4736011cfabca8aa2b59ee6db8e0 https://doi.org/10.1007/bf00773458 Zobrazit plný text záznamu Full text from SpringerLink