Nonlinear Time Series Analysis of Economic and Financial Data (eBook, PDF)

Redaktion: Rothman, Philip

• Format: PDF

Produktbeschreibung

Nonlinear Time Series Analysis of Economic and Financial Data provides an examination of the flourishing interest that has developed in this area over the past decade. The constant theme throughout this work is that standard linear time series tools leave unexamined and unexploited economically significant features in frequently used data sets. The book comprises original contributions written by specialists in the field, and offers a combination of both applied and methodological papers. It will be useful to both seasoned veterans of nonlinear time series analysis and those searching for an informative panoramic look at front-line developments in the area.

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Produktdetails

• Verlag: Springer New York
• Seitenzahl: 373
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781461551294
• Artikelnr.: 44177274

Inhaltsangabe

1 Business Cycle Turning Points: Two Empirical Business Cycle Model Approaches.- 2 A Markov Switching Cookbook.- 3 A Reanalysis Of The Spectral Properties Of Some Economic And Financial Time Series.- 4 Nonlinear Econometric Modelling: A Selective Review.- 5 Unit-Root Tests And Excess Returns.- 6 On The Inherent Nonlinearity Of Frequency Dependent Time Series Relationships.- 7 Stationarity Tests With Multiple Endogenized Breaks.- 8 Nonlinear Evolution In Uk Stock Returns And Volume.- 9 Nonlinear Adjustment Towards Long-Run Money Demand.- 10 Asymmetric Nonlinear Smooth Transition Garch Models.- 11 Testing The Present Value Hypothesis From A Vector Autoregression With Stochastic Regime Switching.- 12 Business Cycle Dynamics: Predicting Transitions With Macrovariables.- 13 Searching For The Sources Of Arch Behavior: Testing The Mixture Of Distributions Model.- 14 Improved Testing And Specification Of Smooth Transition Regression Models.- 15 Speculative Behavior, Regime-Switching, And Stock Market Crashes.- 16 Higher-Order Residual Analysis For Simple Bilinear And Threshold Autoregressive Models With The Tr Test.