Statistical Inference for Nonlinear Processes

Jan Beran, Yuanhua Feng, Sucharita Ghosh and Rafal Kulik
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Jan Beran: University of Konstanz, Dept. of Mathematics and Statistics
Yuanhua Feng: University of Paderborn, Faculty of Business Administration and Economics
Sucharita Ghosh: Swiss Federal Research Institute WSL
Rafal Kulik: University of Ottawa, Dept. of Mathematics and Statistics

Chapter Chapter 6 in Long-Memory Processes, 2013, pp 529-554 from Springer

Abstract: Abstract In this section, we consider nonlinear processes with long memory. We will mainly focus on volatility models: stochastic volatility (see Definitions 2.3–2.4 and Sect. 4.2.6 for limit theorems), ARCH(∞) processes (see Definition 2.1 and Sect. 4.2.7 ) and LARCH(∞) models (see ( 2.47 ) and ( 2.48 ), and Sect. 4.2.8 ). Statistical inference for traffic models is not well developed yet (see Faÿ et al. in Queueing Syst. 54(2):121–140, 2006, Bernoulli 13(2):473–491, 2007; Hsieh et al. in J. Econom. 141(2):913–949, 2007 for some results in this direction).

Keywords: Asymptotic Distribution; Asymptotic Normality; Stochastic Volatility; Stochastic Volatility Model; Tail Index (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-35512-7_6

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DOI: 10.1007/978-3-642-35512-7_6

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