Comparing several methods to compute joint prediction regions for path forecasts generated by vector autoregressions

Stefan Bruder

No 181, ECON - Working Papers from Department of Economics - University of Zurich

Abstract: Path forecasts, defined as sequences of individual forecasts, generated by vector autoregressions are widely used in applied work. It has been recognized that a profound econometric analysis often requires, besides the path forecast, a joint prediction region that contains the whole future path with a prespecified coverage probability. The forecasting literature offers several different methods for computing joint prediction regions, where the existing methods are either bootstrap based or rely on asymptotic results. The aim of this paper is to investigate the finite-sample performance of three methods for constructing joint prediction regions in various scenarios via Monte Carlo simulations.

Keywords: Path forecast; joint prediction region; Monte Carlo simulation (search for similar items in EconPapers)
JEL-codes: C15 C32 C53 (search for similar items in EconPapers)
Date: 2014-11, Revised 2015-12
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:zur:econwp:181

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