Refine
Has Fulltext
- yes (3)
Is part of the Bibliography
- yes (3)
Document Type
- Journal article (3) (remove)
Language
- English (3)
Keywords
- Gaussian approximation (3)
- count time series (3)
- estimation error (2)
- Value at Risk (1)
- coherent forecasting (1)
- expected shortfall (1)
- expectiles (1)
- mid quantiles (1)
- prediction interval (1)
- predictive performance (1)
Institute
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.