Evaluating Approximate Point Forecasting of Count Processes
Publication date
2019
Document type
Research article
Author
Publisher
MDPI
Series or journal
Econometrics
Periodical volume
7
Periodical issue
3/30
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
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.
Cite as
Enthalten in: Econometrics. - Basel : MDPI, 2013. - Online-Ressource. - Bd. 7.2019, 3/30, insges. 28 S.
Version
Not applicable (or unknown)
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