@article{HomburgWeissAlwanetal.2019,
  author    = {Homburg, Annika and Weiß, Christian H. and Alwan, Layth C. and Frahm, Gabriel and G{\"o}b, Rainer},
  title     = {Evaluating approximate point forecasting of count processes},
  series = {Econometrics},
  volume    = {7},
  journal   = {Econometrics},
  number    = {3},
  issn      = {2225-1146},
  doi       = {10.3390/econometrics7030030},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-196929},
  year      = {2019},
  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.},
  language  = {en}
}