TY  - JOUR
A1  - Homburg, Annika
A1  - Weiß, Christian H.
A1  - Alwan, Layth C.
A1  - Frahm, Gabriel
A1  - Göb, Rainer
T1  - Evaluating approximate point forecasting of count processes
T2  - Econometrics
N2  - 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.
KW  - count time series
KW  - estimation error
KW  - Gaussian approximation
KW  - predictive performance
KW  - quantile forecasts
KW  - Value at Risk
Y1  - 2019
UR  - https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/19692
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-196929
SN  - 2225-1146
VL  - 7
IS  - 3
ER  -