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Long Memory Conditional Heteroscedasticity in Count Data

Mawuli Segnon and Manuel Stapper

No 8219, CQE Working Papers from Center for Quantitative Economics (CQE), University of Muenster

Abstract: This paper introduces a new class of integer-valued long memory processes that are adaptations of the well-known FIGARCH(p, d, q) process of Baillie (1996) and HYGARCH(p, d, q) process of Davidson (2004) to a count data setting. We derive the statistical properties of the models and show that reasonable parameter estimates are easily obtained via conditional maximum likelihood estimation. An empirical application with financial transaction data illustrates the practical importance of the models.

Keywords: Count Data; Poisson Autoregression; Fractionally Integrated; INGARCH (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2019-05
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