Robust multivariate and nonlinear time series models : Application of robust estimators for the vector autoregressive and bilinear time series models

Ravi Ramakrishnan

ISBN 10: 3843357811 / ISBN 13: 9783843357814

Verlag: LAP LAMBERT Academic Publishing, 2010

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables like indices and stock prices and global variables like commodity prices are more tightly coupled than ever before. This translates to the use of multivariate or vector time series models and algorithms in analyzing and understanding the relationships that these variables share with each other. While robustness and time series modeling have been vastly researched individually in the past, application of robust methods to estimate time series models is still quite open. The central goal of this thesis is the study of the S-estimator, a robust estimator, applied to some simple vector and nonlinear time series models. In each case, we will look at the important aspect of stationarity of the model and analyze the asymptotic behavior of the S-estimator.

Bestandsnummer des Verkäufers 9783843357814

Bibliografische Details

• Titel: Robust multivariate and nonlinear time series models : Application of robust estimators for the vector autoregressive and bilinear time series models
• Verlag: LAP LAMBERT Academic Publishing
• Erscheinungsjahr: 2010
• Sprache: Englisch
• ISBN-10: 3843357811
• ISBN-13: 9783843357814
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 220B x 150 H x 10 T Millimeter Breite: 220 mm, Höhe: 150 mm, Tiefe: 10 mm
• Gewicht: 250 Gramm

Über diesen Titel

Inhaltsangabe

Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables like indices and stock prices and global variables like commodity prices are more tightly coupled than ever before. This translates to the use of multivariate or vector time series models and algorithms in analyzing and understanding the relationships that these variables share with each other. While robustness and time series modeling have been vastly researched individually in the past, application of robust methods to estimate time series models is still quite open. The central goal of this thesis is the study of the S-estimator, a robust estimator, applied to some simple vector and nonlinear time series models. In each case, we will look at the important aspect of stationarity of the model and analyze the asymptotic behavior of the S-estimator.