Evaluating Approximate Point Forecasting of Count Processes

Autor:	Annika Homburg, Christian H. Weiß, Layth C. Alwan, Gabriel Frahm, Rainer Göb
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	count time series estimation error Gaussian approximation predictive performance quantile forecasts Value at Risk Economics as a science HB71-74
Zdroj:	Econometrics, Vol 7, Iss 3, p 30 (2019)
Druh dokumentu:	article
ISSN:	2225-1146
DOI:	10.3390/econometrics7030030
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/20fe36b675ad4c4cb5c7dda9961d4c9c Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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