Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/247530 
Erscheinungsjahr: 
2019
Quellenangabe: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 7 [Issue:] 3 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-28
Verlag: 
MDPI, Basel
Zusammenfassung: 
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
Schlagwörter: 
count time series
estimation error
Gaussian approximation
predictive performance
quantile forecasts
Value at Risk
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