Evaluating the Calibration of Multi-Step-Ahead Density Forecasts Using Raw Moments
By Malte Knüppel. Social Science Research Network, 2015
69 citations recorded by the source corpus.
The evaluation of multi-step-ahead density forecasts is complicated by the serial correlation of the corresponding probability integral transforms. In the literature, three testing approaches can be found that take this problem into account. However, these approaches rely on data-dependent critical values, ignore important information and, therefore lack power, or suffer from size distortions even asymptotically. This article proposes a new testing approach based on raw moments. It is extremely easy to implement, uses standard critical values, can include all moments regarded as important, and has correct asymptotic size. It is found to have good size and power properties in finite samples if it is based on the (standardized) probability integral transforms.
Subfields: Economics and Econometrics, Finance, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/6176c0dda8c6b786b43af30e6df1addcfc85900a
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In Economics and Econometrics
- Francesco Ravazzolo
- K. Aastveit
- H. V. Dijk
- James Mitchell
- Malte Knüppel
- K. Wallis
- Tilmann Gneiting
- Roopesh Ranjan
In Finance
In Global and Planetary Change
Citations in the corpus
- The Evolution of Forecast Density Combinations in Economics (2018, background): “These forecast densities are not assumed symmetric so that the balances of risks may be on the upside or downside; specifically, the Bank allow the (conditional) mean forecast to differ from the modal forecast by assuming that their forecast density is a two-piece normal distribution; for a detailed”