Evaluating approximate point forecasting of count processes
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- In forecasting count processes, practitioners often ignore the discreteness of counts and compute forecasts based on Gaussian approximations instead. For both central and non-central point forecasts, and for various types of count processes, the performance of such approximate point forecasts is analyzed. The considered data-generating processes include different autoregressive schemes with varying model orders, count models with overdispersion or zero inflation, counts with a bounded range, and counts exhibiting trend or seasonality. WeIn forecasting count processes, practitioners often ignore the discreteness of counts and compute forecasts based on Gaussian approximations instead. For both central and non-central point forecasts, and for various types of count processes, the performance of such approximate point forecasts is analyzed. The considered data-generating processes include different autoregressive schemes with varying model orders, count models with overdispersion or zero inflation, counts with a bounded range, and counts exhibiting trend or seasonality. We conclude that Gaussian forecast approximations should be avoided.…
Autor(en): | Annika Homburg, Christian H. Weiß, Layth C. Alwan, Gabriel Frahm, Rainer Göb |
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URN: | urn:nbn:de:bvb:20-opus-196929 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Fakultät für Mathematik und Informatik / Institut für Mathematik |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Econometrics |
ISSN: | 2225-1146 |
Erscheinungsjahr: | 2019 |
Band / Jahrgang: | 7 |
Heft / Ausgabe: | 3 |
Aufsatznummer: | 30 |
Originalveröffentlichung / Quelle: | Econometrics (2019) 7:3, 30. https://doi.org/10.3390/econometrics7030030 |
DOI: | https://doi.org/10.3390/econometrics7030030 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik |
Freie Schlagwort(e): | Gaussian approximation; Value at Risk; count time series; estimation error; predictive performance; quantile forecasts |
Datum der Freischaltung: | 29.04.2022 |
Datum der Erstveröffentlichung: | 06.07.2019 |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |