Jack Fosten, Daniel Gutknecht, Marc-Oliver Pohle
arXiv 6 Feb 2023 · Econometrics · publishedJournal of Business and Economic Statistics (2024) · 2 citations (OpenAlex)
arXiv:2302.02747 · PDF · DOI · OpenAlex · Extracted main text
Quantile forecasts made across multiple horizons have become an important output of many financial institutions, central banks and international organisations. This paper proposes misspecification tests for such quantile forecasts that assess optimality over a set of multiple forecast horizons and/or quantiles. The tests build on multiple Mincer-Zarnowitz quantile regressions cast in a moment equality framework. Our main test is for the null hypothesis of autocalibration, a concept which assesses optimality with respect to the information contained in the forecasts themselves. We provide an extension that allows to test for optimality with respect to larger information sets and a multivariate extension. Importantly, our tests do not just inform about general violations of optimality, but may also provide useful insights into specific forms of sub-optimality. A simulation study investigates the finite sample performance of our tests, and two empirical applications to financial returns and U.S. macroeconomic series illustrate that our tests can yield interesting insights into quantile forecast sub-optimality and its causes.
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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 | Adrian, T., N. Boyarchenko, and D. Giannone (2019) Vulnerable growth | 0.874 | 5 | 2 | 100% |
| 2 | Gaglianone, W. P., L. R. Lima, O. Linton, and D. R. Smith (2011) Evaluating Value-at-Risk Models via Quantile Regression | 0.843 | 4 | 3 | 75% |
| 3 | Christoffersen, P. F (1998) Evaluating interval forecasts | 0.843 | 4 | 3 | 75% |
| 4 | Engle, R. F. and S. Manganelli (2004) CAViaR: Conditional autoregressive value at risk by regression quantiles | 0.737 | 3 | 3 | 67% |
| 5 | Manzan, S (2015) Forecasting the distribution of economic variables in a data-rich environment | 0.737 | 3 | 2 | 100% |
| 6 | Patton, A. and A. Timmermann (2012) Forecast rationality tests based on multi-horizon bounds | 0.644 | 3 | 2 | 67% |
| 7 | Corradi, V., J. Fosten, and D. Gutknecht (2023) Conditional Quantile Coverage: an Application to Growth-at-Risk | 0.644 | 2 | 2 | 100% |
| 8 | Tsyplakov, A (2013) Evaluation of probabilistic forecasts: proper scoring rules and moments | 0.644 | 2 | 2 | 100% |
| 9 | Gneiting, T. and R. Ranjan (2013) Combining predictive distributions | 0.644 | 2 | 2 | 100% |
| 10 | Pascual, L., J. Romo, and E. Ruiz (2006) Bootstrap prediction for returns and volatilities in GARCH models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 63 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Statistical Inference for Score Decompositions | 0.405 | 1 | 1 |