Homburg, Annika
[Verfasser:in];
Weiß, Christian H.
[Verfasser:in];
Alwan, Layth C.
[Verfasser:in];
Frahm, Gabriel
[Verfasser:in];
Göb, Rainer
[Verfasser:in]
Evaluating approximate point forecasting of count processes
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Medientyp:
E-Artikel
Titel:
Evaluating approximate point forecasting of count processes
Beteiligte:
Homburg, Annika
[Verfasser:in];
Weiß, Christian H.
[Verfasser:in];
Alwan, Layth C.
[Verfasser:in];
Frahm, Gabriel
[Verfasser:in];
Göb, Rainer
[Verfasser:in]
Anmerkungen:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Beschreibung:
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.