Malte Knüppel, Fabian Krüger, Marc-Oliver Pohle
arXiv 29 Nov 2022 · Econometrics · 10 citations (OpenAlex)
arXiv:2211.16362 · PDF · DOI · OpenAlex · Extracted main text
Calibration tests based on the probability integral transform (PIT) are routinely used to assess the quality of univariate distributional forecasts. However, PIT-based calibration tests for multivariate distributional forecasts face various challenges. We propose two new types of tests based on proper scoring rules, which overcome these challenges. They arise from a general framework for calibration testing in the multivariate case, introduced in this work. The new tests have good size and power properties in simulations and solve various problems of existing tests. We apply the tests to forecast distributions for macroeconomic and financial time series data.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Dovern, J. and H. Manner (2020) Order-invariant tests for proper calibration of multivariate density forecasts | 1.000 | 12 | 4 | 100% |
| 2 | Knüppel, M (2015) Evaluating the calibration of multi-step-ahead density forecasts using raw moments | 1.000 | 7 | 4 | 100% |
| 3 | Thorarinsdottir, T. L., M. Scheuerer, and C. Heinz (2016) Assessing the calibration of high-dimensional ensemble forecasts using rank histograms | 1.000 | 6 | 3 | 100% |
| 4 | Gneiting, T., L. I. Stanberry, E. P. Grimit, L. Held, and N. A. John… (2008) Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds | 0.874 | 7 | 2 | 100% |
| 5 | Gneiting, T., F. Balabdaoui, and A. E. Raftery (2007) Probabilistic forecasts, calibration and sharpness | 0.874 | 6 | 2 | 100% |
| 6 | Krüger, F., S. Lerch, T. L. Thorarinsdottir, and T. Gneiting (2021) Predictive inference based on Markov chain Monte Carlo output | 0.843 | 3 | 3 | 100% |
| 7 | Wei, W., F. Balabdaoui, and L. Held (2017) Calibration tests for multivariate Gaussian forecasts | 0.843 | 3 | 3 | 100% |
| 8 | Gneiting, T. and A. E. Raftery (2007) Strictly proper scoring rules, prediction, and estimation | 0.811 | 4 | 2 | 100% |
| 9 | Gneiting, T. and R. Ranjan (2013) Combining predictive distributions | 0.811 | 4 | 2 | 100% |
| 10 | Tsyplakov, A (2011) Evaluating density forecasts: A comment | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 76 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Kernel Score Perspective on Forecast Disagreement and the Linear Pool | 0.843 | 4 | 3 |
| 2 | Uncertainty Quantification in Forecast Comparisons | 0.843 | 3 | 3 |
| 3 | 1.4cm bred From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks | 0.737 | 4 | 2 |