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DOI: 10.5445/IR/1000084976
Veröffentlicht am 02.08.2018

High Dimensional Time Series — New Techniques and Applications

Liang, Chong

Abstract (englisch):
The past decade witnessed the rapid development of high dimensional statistics in deterministic design. High dimensional time series analysis, due to the time dependency, still faces several theoretical challenges. Among the time series models, the Vector Error Correction Model (VECM) is especially complicated because of the non-stationary components. The classical estimation strategies (e.g. Johansen's approach) fail to provide consistent estimates for dimensions larger than three. Moreover, it is impossible to apply existing statistical methods to determine VECM in high dimensions, i.e. when the dimension is allowed to increase with the number of observations or even larger than that.
This dissertation aims at providing feasible regularized methods, which can determine and estimate high dimensional VECM with robust statistical properties. The detailed analysis is divided into three parts. First I develop new tailored Lasso-type methods to estimate VECM and prove their statistical properties, in a setting where the cointegration rank is fixed but unknown and the dimension is large but not increasing with sample size. Then this m ... mehr

Zugehörige Institution(en) am KIT Institut für Volkswirtschaftslehre (ECON)
Publikationstyp Hochschulschrift
Jahr 2018
Sprache Englisch
Identifikator URN: urn:nbn:de:swb:90-849767
KITopen ID: 1000084976
Verlag Karlsruhe
Umfang VI, 176 S.
Abschlussart Dissertation
Fakultät Fakultät für Wirtschaftswissenschaften (WIWI)
Institut Institut für Volkswirtschaftslehre (ECON)
Prüfungsdatum 18.07.2018
Referent/Betreuer Prof. M. Schienle
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