Path Forecast Evaluation

Oscar Jorda () and Massimiliano Marcellino

Working Papers from University of California at Davis, Department of Economics

Abstract: A path forecast refers to the sequence of forecasts 1 to H periods into the future. A summary of the range of possible paths the predicted variable may follow for a given confidence level requires construction of simultaneous confidence regions that adjust for any covariance between the elements of the path forecast. This paper shows how to construct such regions with the joint predictive density and Scheffe's (1953) S-method. In addition, the joint predictive density can be used to construct simple statistics to evaluate the local internal consistency of a forecasting exercise of a system of variables. Monte Carlo simulations demonstrate that these simultaneous confidence regions provide approximately correct coverage in situations where traditional error bands, based on the collection of marginal predictive densities for each horizon, are vastly off mark. The paper showcases these methods with an application to the most recent monetary episode of interest rate hikes in the U.S. macroeconomy.

JEL-codes: C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets and nep-for
Date: 2008-07
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Related works:
Working Paper: Path Forecast Evaluation (2008)
Working Paper: Path Forecast Evaluation (2008)
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Persistent link: http://EconPapers.repec.org/RePEc:ecl:ucdeco:08-5

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