Evaluating approximate point forecasting of count processes
Publication date
2019-07-06
Document type
Research article
Author
Publisher
MDPI
Series or journal
Econometrics
ISSN
Periodical volume
7
Periodical issue
3
Article ID
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.
Description
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Version
Published version
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Metadata only access