MULTIPERIOD BAYESIAN FORECASTS FOR AR MODELS

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/27819

Title:	MULTIPERIOD BAYESIAN FORECASTS FOR AR MODELS
Authors:	LIU,SI
Contributors:	統計研究所
Keywords:	TIME-SERIES MODELS
Date:	1994
Issue Date:	2010-06-29 19:34:19 (UTC+8)
Publisher:	中央大學
Abstract:	The multiperiod Bayesian forecast under the normal-gamma prior assumption for univariate AR models with strongly exogenous variables is investigated. A two-stage approximate method is proposed to provide an estimator of the posterior predictive density for any future observation in a convenient closed form. Some properties of the proposed method are proven analytically for a one-step ahead forecast. The precision of the proposed method is examined by using some simulated data and two sets of real data up to lead-twelve-ahead forecasts by comparison with a path sampling method. It is found that most of the results for the two discussed methods are rather close for short period forecast. Especially when sample size is sufficiently large, the estimated predictive density provided by the two-stage method asymptotically converges to the true density. A heuristic proof of this asymptotic property is also presented.
Relation:	ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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