.png){kind=icon}
.png){kind=icon}
Return
Testing Interval Forecasts: A GMM-Based Approach
.png){kind=icon}
OA
AI
DOI：10.1002/for.1260.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a new evaluation framework for interval forecasts. Our model-free test can be used to evaluate interval forecasts and high-density regions, potentially discontinuous and/or asymmetric. Using a simple J-statistic, based on the moments defined by the orthonormal polynomials associated with the binomial distribution, this new approach presents many advantages. First, its implementation is extremely easy. Second, it allows for a separate test for unconditional coverage, independence and conditional coverage hypotheses. Third, Monte Carlo simulations show that for realistic sample sizes our GMM test has good small-sample properties. These results are corroborated by an empirical application on SP500 and Nikkei stock market indexes. It confirms that using this GMM test leads to major consequences for the ex post evaluation of interval forecasts produced by linear versus nonlinear models. Copyright (c) 2011 John Wiley & Sons, Ltd.
Keywords:
interval forecasts
high-density region
GMM
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.7
Papers:
2.3K
Citations:
3.0K