In this paper we analyze two different approaches for modeling dependent count data with long-memory. The first model we consider explicitly takes into account the integer nature of data and the long-range correlation, while the second model is a count-data long-memory model where the distribution of the current observation is specified conditionally upon past observations. We compare these two different models by looking at their estimation and forecasting performances.

Long-memory models for count time series

Luisa Bisaglia;Massimiliano Caporin;Matteo Grigoletto
2020

Abstract

In this paper we analyze two different approaches for modeling dependent count data with long-memory. The first model we consider explicitly takes into account the integer nature of data and the long-range correlation, while the second model is a count-data long-memory model where the distribution of the current observation is specified conditionally upon past observations. We compare these two different models by looking at their estimation and forecasting performances.
Book of short papers SIS 2020
9788891910776
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3378011
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact