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dc.contributor.authorHürlimann, Werneren
dc.date.accessioned2012-03-22-
dc.date.accessioned2012-05-22T16:45:40Z-
dc.date.available2012-05-22T16:45:40Z-
dc.date.issued2012-
dc.identifier.citation|aJournal of Statistical and Econometric Methods|c2241-0376|v1|h1|nInternational Scientific Press|y2012|p93-101en
dc.identifier.urihttp://hdl.handle.net/10419/58010-
dc.description.abstractA severe limitation of the original autoregressive process of order one or AR(1) process is the Gaussian nature of the assumed residual error distribution while the observed sample residual errors tend to be much more skewed and have a much higher kurtosis than is allowed by a normal distribution. Four non-Gaussian noise specifications are considered, namely the normal inverse Gaussian, the skew Student t, the normal Laplace and the reshaped Hermite-Gauss distributions. Besides predictive distributional properties of some of these AR(1) processes, an in-depth analysis of the fitting capabilities of these models is undertaken. For the Swiss consumer price index, it is shown that the AR(1) with normal Laplace (NL) noise has the best goodness-of-fit in a dual sense for four types of estimators. On the one hand the moment estimators of the NL residual error distribution yield the smallest Anderson-Darling, Cramér-von Mises and chi-square statistics, and on the other hand the minimum of these three statistics is also reached by the NL distribution.en
dc.language.isoengen
dc.publisher|aInternational Scientific Pressen
dc.subject.ddc330en
dc.subject.keywordforce of inflationen
dc.subject.keywordAR(1)en
dc.subject.keywordnormal inverse Gaussianen
dc.subject.keywordskew student ten
dc.subject.keywordnormal laplaceen
dc.subject.keywordHermite-Gaussen
dc.subject.keywordBera-Jarque statisticen
dc.subject.keywordCramér-von Mises statisticen
dc.subject.keywordAnderson-Darling statisticen
dc.titleOn non-Gaussian AR(1) inflation modeling-
dc.typeArticleen
dc.identifier.ppn689055471en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
econstor.citation.journaltitleJournal of Statistical and Econometric Methodsen
econstor.citation.issn2241-0376en
econstor.citation.volume1en
econstor.citation.issue1en
econstor.citation.publisherInternational Scientific Pressen
econstor.citation.year2012en
econstor.citation.startpage93en
econstor.citation.endpage101en

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