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2010-08-03Buch DOI: 10.18452/4267
High Dimensional Nonstationary Time Series Modelling with Generalized Dynamic Semiparametric Factor Model
dc.contributor.authorSong, Song
dc.contributor.authorHärdle, Wolfgang Karl
dc.contributor.authorRitov, Ya‘acov
dc.date.accessioned2017-06-16T00:11:13Z
dc.date.available2017-06-16T00:11:13Z
dc.date.created2010-09-09
dc.date.issued2010-08-03
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4919
dc.description.abstract(High dimensional) time series which reveal nonstationary and possibly periodic behavior occur frequently in many fields of science. In this article, we separate the modeling of high dimensional time series to time propagation of low dimensional time series and high dimensional time invariant functions via functional factor analysis. We propose a two-step estimation procedure. At the first step, we detect the deterministic trends of the time series by incorporating time basis selected by the group Lasso-type technique and choose the space basis based on smoothed functional principal component analysis. We show properties of this estimator under various situations extending current variable selection studies. At the second step, we obtain the detrended low dimensional stochastic process, but it also poses an important question: is it justified, from an inferential point of view, to base further statistical inference on the estimated stochastic time series? We show that the difference of the inference based on the estimated time series and "true" unobserved time series is asymptotically negligible, which finally allows one to study the dynamics of the whole high-dimensional system with a low dimensional representation together with the deterministic trend. We apply the method to our motivating empirical problems: studies of the dynamic behavior of temperatures (further used for pricing weather derivatives), implied volatilities and risk patterns and correlated brain activities (neuro-economics related) using fMRI data, where a panel version model is also presented.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectfMRIeng
dc.subjectSemiparametric modeleng
dc.subjectFactor modeleng
dc.subjectGroup Lassoeng
dc.subjectSeasonalityeng
dc.subjectSpectral Analysiseng
dc.subjectPeriodiceng
dc.subjectAsymptotic inferenceeng
dc.subjectWeathereng
dc.subjectImplied Volatility Surfaceeng
dc.subject.ddc330 Wirtschaft
dc.titleHigh Dimensional Nonstationary Time Series Modelling with Generalized Dynamic Semiparametric Factor Model
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-100174451
dc.identifier.doihttp://dx.doi.org/10.18452/4267
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages34
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2010
local.edoc.container-issue39
local.edoc.container-year2010
local.edoc.container-erstkatid2195055-6

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