Essays in Nonlinear Time Series Econometrics by Niels HaldrupEssays in Nonlinear Time Series Econometrics by Niels Haldrup

Essays in Nonlinear Time Series Econometrics

EditorNiels Haldrup, Mika Meitz, Pentti Saikkonen

Hardcover | July 26, 2014

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This edited collection concerns nonlinear economic relations that involve time. It is divided into four broad themes that all reflect the work and methodology of Professor Timo Terasvirta, one of the leading scholars in the field of nonlinear time series econometrics. The themes are: Testingfor linearity and functional form, specification testing and estimation of nonlinear time series models in the form of smooth transition models, model selection and econometric methodology, and finally applications within the area of financial econometrics. All these research fields include contributions that represent state of the art in econometrics such as testing for neglected nonlinearity in neural network models, time-varying GARCH and smooth transition models, STAR models and common factors in volatility modeling, semi-automatic general tospecific model selection for nonlinear dynamic models, high-dimensional data analysis for parametric and semi-parametric regression models with dependent data, commodity price modeling, financial analysts earnings forecasts based on asymmetric loss function, local Gaussian correlation and dependencefor asymmetric return dependence, and the use of bootstrap aggregation to improve forecast accuracy. Each chapter represents original scholarly work, and reflects the intellectual impact that Timo Terasvirta has had and will continue to have, on the profession.
Niels Haldrup is Professor of Economics at Aarhus University. He is director of CREATES, a research center of excellence funded by the Danish National Research Foundation. He has published widely in Journals such as Journal of Econometrics, Journal of Applied Econometrics, Journal of Business and Economic Statistics, and Econometric Th...
Title:Essays in Nonlinear Time Series EconometricsFormat:HardcoverDimensions:352 pages, 9.21 × 6.14 × 0.03 inPublished:July 26, 2014Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199679959

ISBN - 13:9780199679959

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Table of Contents

PrefaceTesting for Linearity and Functional Form1. Jin Seo Cho, Isao Ishida, and Halbert White: Testing for Neglected Nonlinearity Using Twofold Unidentified Models under the Null and Hexic Expansions2. James Davidson and Andreea G. Halunga: Consistent Testing of Functional Form in Time Series Models3. Robinson Kruse and Rickard Sandberg: Linearity Testing for Trending Data with an Application of the Wild BootstrapSmooth Transition Models4. Heather M. Anderson and Farshid Vahid: Common Non-linearities in Multiple Series of Stock Market Volatility5. Katarina Juselius and Mikael Juselius: Balance Sheet Recessions and Time-Varying Coefficients in a Phillips Curve Relationship: An Application to Finnish Data6. Cristina Amado and Helina Laakkonen: Modelling Time-Varying Volatility in Financial Returns: Evidence from Bond MarketsModel Selection and Econometric Methodology7. Jennifer L. Castle and David F. Hendry: Semi-automatic Non-linear Model Selection8. Helmut Lutkepohl: Fundamental Problems with Nonfundamental Shocks9. Marcelo C. Medeiros and Eduardo F. Mendes: Penalized Estimation of Semi-parametric Additive Time Series Models10. Laurent A. F. Callot and Anders Bredahl Kock: Oracle Efficient Estimation and Forecasting with the Adaptive Lasso and the Adaptive Group Lasso in Vector AutoregressionsApplied Financial Econometrics11. Robert Engle: Modeling Commodity Prices with Dynamic Conditional Beta12. Marco Aiolfi, Marius Rodriguez, and Allan Timmermann: Bias and Uncertainty in Analyst Earnings Expectations at Different Forecast Horizons13. Bard Stove and Dag Tjostheim: Asymmetric Dependence Patterns in Financial Returns: An Empirical Investigation Using Local Gaussian Correlation14. Eric Hillebrand, Tae-Hwy Lee, and Marcelo C. Medeiros: Bagging Constrained Equity Premium Predictors