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Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models

Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models

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BERAN, Jan, Yuanhua FENG, Sucharita GHOSH, 2015. Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models. In: Statistical Papers. 56(2), pp. 431-451. ISSN 0932-5026. eISSN 1613-9798

@article{Beran2015Model-29162, title={Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models}, year={2015}, doi={10.1007/s00362-014-0590-x}, number={2}, volume={56}, issn={0932-5026}, journal={Statistical Papers}, pages={431--451}, author={Beran, Jan and Feng, Yuanhua and Ghosh, Sucharita} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/29162"> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/29162"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-10-22T08:47:35Z</dc:date> <dc:creator>Beran, Jan</dc:creator> <dc:contributor>Beran, Jan</dc:contributor> <dc:creator>Ghosh, Sucharita</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-10-22T08:47:35Z</dcterms:available> <dcterms:title>Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models</dcterms:title> <dcterms:issued>2015</dcterms:issued> <dc:contributor>Feng, Yuanhua</dc:contributor> <dc:creator>Feng, Yuanhua</dc:creator> <dc:language>eng</dc:language> <dc:contributor>Ghosh, Sucharita</dc:contributor> <dcterms:abstract xml:lang="eng">Duration series often exhibit long-range dependence and local nonstationarities. Here, exponential FARIMA (EFARIMA) and exponential SEMIFAR (ESEMIFAR) models are introduced. These models capture simultaneously nonstationarities in the mean as well as short- and long-range dependence, while avoiding the complication of unobservable latent processes. The models can be thought of as locally stationary long-memory extensions of exponential ACD models. Statistical properties of the models are derived. In particular the long-memory parameter in the original and the log-transformed process is the same. For Gaussian innovations, exact explicit formulas for all moments and autocovariances are given, and the unconditional distribution is log-normal. Estimation and model selection can be carried out with standard software. The approach is illustrated by an application to average daily transaction durations.</dcterms:abstract> </rdf:Description> </rdf:RDF>

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