Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/126605
Authors: 
Bruder, Stefan
Year of Publication: 
2015
Series/Report no.: 
Working Paper Series, University of Zurich, Department of Economics 181 [rev.]
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.
Subjects: 
Path Forecast
Joint Prediction Region
Monte Carlo Simulation
JEL: 
C15
C32
C53
Document Type: 
Working Paper

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