Bootstrap Confidence Bands for Forecast Paths

Anna Staszewska-Bystrova

No 24, Working Papers from COMISEF

Abstract: The problem of forecasting from vector autoregressive models has attracted considerable attention in the literature. The most popular non-Bayesian approaches use large sample normal theory or the bootstrap to evaluate the uncertainty associated with the forecast. The literature has concentrated on the problem of assessing the uncertainty of the prediction for a single period. This paper considers the problem of how to assess the uncertainty when the forecasts are done for a succession of periods. It describes and evaluates bootstrap method for constructing confidence bands for forecast paths. The bands are constructed from forecast paths obtained in bootstrap replications with an optimisation procedure used to find the envelope of the most concentrated paths. The method is shown to have good coverage properties in a Monte Carlo study.

Keywords: vector autoregression; forecast path; bootstrapping; simultaneous statistical inference (search for similar items in EconPapers)
JEL-codes: C15 C32 C53 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2009-12-07
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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