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- Title
Evaluating Approximate Point Forecasting of Count Processes.
- Authors
Homburg, Annika; Weiß, Christian H.; Alwan, Layth C.; Frahm, Gabriel; Göb, Rainer
- Abstract
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.
- Subjects
COUNTING; VALUE at risk
- Publication
Econometrics (2225-1146), 2019, Vol 7, Issue 3, p30
- ISSN
2225-1146
- Publication type
Article
- DOI
10.3390/econometrics7030030