|Title:||Evaluating Approximate Point Forecasting of Count Processes||Authors:||Homburg, Annika
Weiß, Christian H.
Alwan, Layth C.
|Affiliation:||University of Wisconsin-Milwaukee
University of Würzburg
|Language:||en||Keywords:||Universitätsbibliographie;Evaluation 2019||Issue Date:||2019||Publisher:||MDPI||Document Type:||Article||Source:||Enthalten in: Econometrics. - Basel : MDPI, 2013. - Online-Ressource. - Bd. 7.2019, 3/30, insges. 28 S.||Journal / Series / Working Paper (HSU):||Econometrics||Volume:||7||Issue:||3/30||Pages:||insges. 28||Publisher Place:||Basel||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.
|Organization Units (connected with the publication):||Quantitative Methoden der Wirtschaftswissenschaften
Angewandte Stochastik und Risikomanagement
|Appears in Collections:||2019|
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