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Open AccessArticle

Evaluating Approximate Point Forecasting of Count Processes

1
Department of Mathematics and Statistics, Helmut Schmidt University, 22043 Hamburg, Germany
2
Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
3
Institute of Mathematics, Department of Statistics, University of Würzburg, 97070 Würzburg, Germany
*
Author to whom correspondence should be addressed.
Econometrics 2019, 7(3), 30; https://doi.org/10.3390/econometrics7030030
Received: 1 April 2019 / Revised: 21 June 2019 / Accepted: 3 July 2019 / Published: 6 July 2019
(This article belongs to the Special Issue Discrete-Valued Time Series: Modelling, Estimation and Forecasting)
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. View Full-Text
Keywords: count time series; estimation error; Gaussian approximation; predictive performance; quantile forecasts; Value at Risk count time series; estimation error; Gaussian approximation; predictive performance; quantile forecasts; Value at Risk
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Homburg, A.; Weiß, C.H.; Alwan, L.C.; Frahm, G.; Göb, R. Evaluating Approximate Point Forecasting of Count Processes. Econometrics 2019, 7, 30.

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