Prediction Intervals for Arima Models

The problem of constructing prediction intervals for linear time series (ARIMA) models is examined. The aim is to find prediction intervals which incorporate an allowance for sampling error associated with parameter estimates. The effect of constraints on parameters arising from stationary and invertibility conditions is also incorporated. Two new methods, based to varying degrees on first-order Taylor approximations, are proposed.

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Year of publication:	1997
Authors:	Snyder, R.D. ; Ord, J.K. ; Koehler, A.B.
Institutions:	Department of Econometrics and Business Statistics, Monash Business School
Subject:	STATISTICS \| ECONOMETRICS

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Series:	Monash Econometrics and Business Statistics Working Papers.
Type of publication:	Book / Working Paper
Notes:	Number 8/97 29 pages
Classification:	C11 - Bayesian Analysis ; C13 - Estimation
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005581130