TY  - JOUR
A1  - Homburg, Annika
A1  - Weiß, Christian H.
A1  - Alwan, Layth C.
A1  - Frahm, Gabriel
A1  - Göb, Rainer
T1  - A performance analysis of prediction intervals for count time series
JF  - Journal of Forecasting
N2  - One of the major motivations for the analysis and modeling of time series data is the forecasting of future outcomes. The use of interval forecasts instead of point forecasts allows us to incorporate the apparent forecast uncertainty. When forecasting count time series, one also has to account for the discreteness of the range, which is done by using coherent prediction intervals (PIs) relying on a count model. We provide a comprehensive performance analysis of coherent PIs for diverse types of count processes. We also compare them to approximate PIs that are computed based on a Gaussian approximation. Our analyses rely on an extensive simulation study. It turns out that the Gaussian approximations do considerably worse than the coherent PIs. Furthermore, special characteristics such as overdispersion, zero inflation, or trend clearly affect the PIs' performance. We conclude by presenting two empirical applications of PIs for count time series: the demand for blood bags in a hospital and the number of company liquidations in Germany.
KW  - coherent forecasting
KW  - count time series
KW  - estimation error
KW  - Gaussian approximation
KW  - prediction interval
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-217906
VL  - 40
IS  - 4
SP  - 603
EP  - 609
ER  - 
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
JF  - 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
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-196929
SN  - 2225-1146
VL  - 7
IS  - 3
ER  -