Evaluating Approximate Point Forecasting of Count Processes - Annika Homburg; Christian H. Weiß; Layth C. Alwan; Gabriel Frahm; Rainer Göb (Econometrics, 2019)
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: 330; count time series; estimation error; Gaussian approximation; predictive performance; quantile forecasts; Value at Risk