Prediction Intervals for Arima Models
Ralph Snyder (),
Keith Ord () and
No 8/97, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
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.
Keywords: STATISTICS; ECONOMETRICS (search for similar items in EconPapers)
JEL-codes: C11 C13 (search for similar items in EconPapers)
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Journal Article: Prediction Intervals for ARIMA Models (2001)
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