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Evaluating Approximate Point Forecasting of Count Processes

Annika Homburg (), Christian H. Weiß (), Layth C. Alwan (), Gabriel Frahm () and Rainer Göb ()
Additional contact information
Annika Homburg: Department of Mathematics and Statistics, Helmut Schmidt University, 22043 Hamburg, Germany
Christian H. Weiß: Department of Mathematics and Statistics, Helmut Schmidt University, 22043 Hamburg, Germany
Layth C. Alwan: Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
Gabriel Frahm: Department of Mathematics and Statistics, Helmut Schmidt University, 22043 Hamburg, Germany
Rainer Göb: Institute of Mathematics, Department of Statistics, University of Würzburg, 97070 Würzburg, Germany

Econometrics, 2019, vol. 7, issue 3, 1-28

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.

Keywords: count time series; estimation error; Gaussian approximation; predictive performance; quantile forecasts; Value at Risk (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2019
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