Title: | Evaluating Approximate Point Forecasting of Count Processes | Authors: | Homburg, Annika Weiß, Christian H. Alwan, Layth C. Frahm, Gabriel Göb, Rainer |
Language: | eng | 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 | 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 |
Publisher DOI: | 10.3390/econometrics7030030 |
Appears in Collections: | 3 - Publication references (without fulltext) |
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