Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/111239
Authors: 
Bruder, Stefan
Year of Publication: 
2014
Series/Report no.: 
Working Paper Series, University of Zurich, Department of Economics 181
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 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 of 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
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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