Forecasting time series by long-memory models for count data with an application to price jumps

Luisa Bisaglia (), Massimiliano Caporin () and Matteo Grigoletto ()
Additional contact information
Luisa Bisaglia: University of Padova
Massimiliano Caporin: University of Padova
Matteo Grigoletto: University of Padova

AStA Advances in Statistical Analysis, 2025, vol. 109, issue 3, No 2, 417-441

Abstract: Abstract We discuss the estimation and forecast of long-memory models for count data time series. We first demonstrate by Monte Carlo simulations that the Whittle estimator is the most appropriate for recovering the memory degree of a count data time series. In the following, we introduce the possibility of forecasting count data by exploiting the infinite autoregressive representation of the model. We complete our analysis with an empirical example in which we verify the predictability of the price jump numbers.

Keywords: Count time series; Long-memory; GLM; Estimation; Forecasting (search for similar items in EconPapers)
JEL-codes: C58 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10182-025-00538-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:alstar:v:109:y:2025:i:3:d:10.1007_s10182-025-00538-1

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10182/PS2

DOI: 10.1007/s10182-025-00538-1

Access Statistics for this article

AStA Advances in Statistical Analysis is currently edited by Göran Kauermann and Yarema Okhrin

More articles in AStA Advances in Statistical Analysis from Springer, German Statistical Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().