Title: Evaluating Approximate Point Forecasting of Count Processes
Authors: Homburg, Annika 
Weiß, Christian H. 
Alwan, Layth C. 
Frahm, Gabriel  
Göb, Rainer 
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
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 
URL: https://ub.hsu-hh.de/DB=1.8/XMLPRS=N/PPN?PPN=1669531112
Appears in Collections:2019

Show full item record

CORE Recommender

Google ScholarTM


Items in openHSU are protected by copyright, with all rights reserved, unless otherwise indicated.