Yannick Hoga, Timo Dimitriadis
arXiv 21 Jun 2021 · Econometrics · publishedJournal of Business and Economic Statistics (2021) · 5 citations (OpenAlex)
arXiv:2106.11104 · PDF · DOI · OpenAlex · Extracted main text
Loss functions are widely used to compare several competing forecasts. However, forecast comparisons are often based on mismeasured proxy variables for the true target. We introduce the concept of exact robustness to measurement error for loss functions and fully characterize this class of loss functions as the Bregman class. For such exactly robust loss functions, forecast loss differences are on average unaffected by the use of proxy variables and, thus, inference on conditional predictive ability can be carried out as usual. Moreover, we show that more precise proxies give predictive ability tests higher power in discriminating between competing forecasts. Simulations illustrate the different behavior of exactly robust and non-robust loss functions. An empirical application to US GDP growth rates demonstrates that it is easier to discriminate between forecasts issued at different horizons if a better proxy for GDP growth is used.
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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 | Laurent S, Rombouts JV, Violante F (2013) On loss functions and ranking forecasting performances of multivariate volatility models | 1.000 | 10 | 3 | 100% |
| 2 | Giacomini R, White H (2006) Tests of conditional predictive ability | 0.971 | 12 | 5 | 92% |
| 3 | Gneiting T (2011) Making and evaluating point forecasts | 0.941 | 6 | 4 | 83% |
| 4 | Patton AJ (2011) Volatility forecast comparison using imperfect volatility proxies | 0.874 | 20 | 2 | 100% |
| 5 | Aruoba SB, Diebold FX, Nalewaik J, Schorfheide F, Song D (2016) Improving | 0.874 | 6 | 2 | 100% |
| 6 | Diebold FX, Mariano RS (1995) Comparing predictive accuracy | 0.737 | 3 | 2 | 100% |
| 7 | Hansen PR, Lunde A (2005) A forecast comparison of volatility models: Does anything beat a GARCH(1, 1)? | 0.737 | 3 | 2 | 100% |
| 8 | Hiriart-Urrut JB, Lemaréchal C (2001) Fundamentals of Convex Analysis | 0.644 | 3 | 2 | 67% |
| 9 | Andersen TG, Bollerslev T, Christoffersen PF, Diebold FX (2013) Financial risk measurement for financial risk management | 0.644 | 2 | 2 | 100% |
| 10 | Bräuning F, Koopman SJ (2014) Forecasting macroeconomic variables using collapsed dynamic factor analysis | 0.644 | 2 | 2 | 100% |
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