Readings in Unobserved Components Models

Paperback | February 9, 2006

EditorAndrew Harvey, Tommaso Proietti

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This title includes the following features: Harvey is a well known author; Includes substantive introductions to each section

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This title includes the following features: Harvey is a well known author; Includes substantive introductions to each section

Andrew Harvey is Professor of Econometrics at the University of Cambridge. Tommaso Proietti is Professor of Economic Statistics at the University of Udine, Italy

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Format:PaperbackDimensions:472 pages, 9.21 × 6.14 × 1.05 inPublished:February 9, 2006Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199278695

ISBN - 13:9780199278695

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Table of Contents

Signal Extraction and Likelihood Inference for Linear UC Models1. Introduction2. P. Burridge and K.F. Wallis: Prediction Theory for Autoregressive-Moving Average Processes3. S.J. Koopman: Exact Initial Kalman Filtering and Smoothing for Non-stationary Time Series Models4. P. de Jong: Smoothing and Interpolation with the State Space Model5. A.C. Harvey and S.J. Koopman: Diagnostic Checking of Unobserved Components in Time Series Models6. R. Kohn, C.F. Ansley and C. Wong: Nonparametric Spline Regression with Autoregressive Moving Average ErrorsUnobserved Components in Economic Time Series7. Introduction8. M.W. Watson: Univariate Detrending Methods with Stochastic Trends9. A.C. Harvey and A. Jaeger: Detrending, Stylized Facts and the Business Cycle10. A. Maravall: Stochastic Linear Trends, Models and Estimators11. D. Pfeffermann: Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys12. A.C. Harvey, S.J. Koopman and M. Riani: The Modelling and Seasonal Adjustment of Weekly ObservationsTesting in Unobserved Components Models13. Introduction14. J. Nyblom: Testing for Deterministic Linear Trends in a Times Series15. F. Canova and B.E. Hansen: Are Seasonal Patterns Stable Over Time? A Test for Seasonal StabilityNon-Linear and Non- Gaussian Models16. Introduction17. A.C. Harvey and C. Fernandes: Times Series Models for Count Data or Qualitative Observations18. Carter and Kohn: On Gibbs Sampling for State Space Models19. P. de Jong and N. Shephard: The Simulation Smoother20. N. Shephard and M.K. Pitt: Likelihood Analysis of Non-Gaussian Measurement Time Series21. J. Durbin and S.J. Koopman: Time Series Analysis of Non-Gaussian Observations based on State Space Models from both Classical and Bayesian Perspectives22. S. Kim, N. Shephard, and S. Chib: Stochastic Volatility: Liklihood Inference and Comparison with ARCH Models23. A. Doucet, S.J. Godsill, and C. Andrieu: On Sequential Monte Carlo Sampling Methods for Bayesian Filtering

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...can be recommended to all those researchers engaged in this field, either on a more theoretical basis or with more emphasis on practical issues.