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Type Journal Article
Scope Discipline-based scholarship
Title A new goodness of fit test for event forecasting and its application to credit default
Organization Unit
Authors
  • Markus Leippold
  • Andreas Bloechlinger
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Management Science
Publisher Institute for Operations Research and the Management Science
Geographical Reach international
ISSN 0025-1909
Volume 57
Number 3
Page Range 487 - 505
Date 2011
Abstract Text We develop a new goodness-of-fit test for validating the performance of probability forecasts. Our test statistic is particularly powerful under sparseness and dependence in the observed data. To build our test statistic, we start from a formal definition of calibrated forecasts, which we operationalize by introducing two components. The first component tests the level of the estimated probabilities. The second component validates the shape, measuring the differentiation between high and low robability events. After constructing test statistics for both level and shape, we provide a global goodness-of-fit statistic, which is asymptotically x^2 distributed. In a simulation exercise, we find that our approach is correctly sized and more powerful than alternative statistics. In particular, our shape statistic is significantly more powerful than the Kolmogorov-Smirnov test. Under independence our global test has significantly greater power than the popular Hosmer and Lemeshow's x^2 test. Moreover, even under dependence our global test remains correctly sized and consistent. As a timely and important empirical application of our method, we study the validation of a forecasting model for credit default events.
Official URL http://pubsonline.informs.org/doi/abs/10.1287/mnsc.1100.1283
Digital Object Identifier 10.1287/mnsc.1100.1283
Other Identification Number merlin-id:4459
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