Artikel
Evaluating Approximate Point Forecasting of Count Processes
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. We conclude that Gaussian forecast approximations should be avoided
- Sprache
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Englisch
- Erschienen in
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 7 ; Year: 2019 ; Issue: 3 ; Pages: 1-28 ; Basel: MDPI
- Klassifikation
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Wirtschaft
- Thema
-
count time series
estimation error
Gaussian approximation
predictive performance
quantile forecasts
Value at Risk
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Homburg, Annika
Weiß, Christian H.
Alwan, Layth C.
Frahm, Gabriel
Göb, Rainer
- Ereignis
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Veröffentlichung
- (wer)
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MDPI
- (wo)
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Basel
- (wann)
-
2019
- DOI
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doi:10.3390/econometrics7030030
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Homburg, Annika
- Weiß, Christian H.
- Alwan, Layth C.
- Frahm, Gabriel
- Göb, Rainer
- MDPI
Entstanden
- 2019
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