Long Memory Conditional Heteroscedasticity in Count Data
Segnon Mawuli, Stapper Manuel
Zusammenfassung
This paper introduces a new class of integer-valued long memory processesthat are adaptations of the well-known FIGARCH(p, d, q) process of Baillie (1996) andHYGARCH(p, d, q) process of Davidson (2004) to a count data setting. We derive thestatistical properties of the models and show that reasonable parameter estimates areeasily obtained via conditional maximum likelihood estimation. An empirical application with financial transaction data illustrates the practical importance of the models.
Schlüsselwörter
Count Data; Poisson Autoregression; Fractionally Integrated; INGARCH
Publikationstyp
Sonstige wissenschaftliche Veröffentlichung
Publikationsstatus
Veröffentlicht
Jahr
2019
Band
82/2019
Reihe
CQE-Working-Papers
Verlag
Center for Quantitative Economics (CQE), University of Muenster
Ort
University of Muenster
Sprache
Englisch
Gesamter Text