This volume presents a collection of readings which give the reader an idea of the nature and scope of unobserved components (UC) models and the methods used to deal with them. The book is intended to give a self-contained presentation of the methods and applicative issues. Harvey has made major contributions to this field and provides substantial introductions throughout the book to form a unified view of the literature.
This volume presents a collection of readings which give the reader an idea of the nature and scope of unobserved components (UC) models and the methods used to deal with them. The book is intended to give a self-contained presentation of the methods and applicative issues. Harvey has made major contributions to this field and provides substantial introductions throughout the book to form a unified view of the literature.
Andrew Harvey is Professor of Econometrics at the University of Cambridge. Tommaso Proietti is Professor of Economic Statistics at the University of Udine, Italy
Inhaltsangabe
* Signal Extraction and Likelihood Inference for Linear UC Models * 1: Introduction * 2: P. Burridge and K.F. Wallis: Prediction Theory for Autoregressive-Moving Average Processes * 3: S.J. Koopman: Exact Initial Kalman Filtering and Smoothing for Non-stationary Time Series Models * 4: P. de Jong: Smoothing and Interpolation with the State Space Model * 5: A.C. Harvey and S.J. Koopman: Diagnostic Checking of Unobserved Components in Time Series Models * 6: R. Kohn, C.F. Ansley and C. Wong: Nonparametric Spline Regression with Autoregressive Moving Average Errors * Unobserved Components in Economic Time Series * 7: Introduction * 8: M.W. Watson: Univariate Detrending Methods with Stochastic Trends * 9: A.C. Harvey and A. Jaeger: Detrending, Stylized Facts and the Business Cycle * 10: A. Maravall: Stochastic Linear Trends, Models and Estimators * 11: D. Pfeffermann: Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys * 12: A.C. Harvey, S.J. Koopman and M. Riani: The Modelling and Seasonal Adjustment of Weekly Observations * Testing in Unobserved Components Models * 13: Introduction * 14: J. Nyblom: Testing for Deterministic Linear Trends in a Times Series * 15: F. Canova and B.E. Hansen: Are Seasonal Patterns Stable Over Time? A Test for Seasonal Stability * Non-Linear and Non- Gaussian Models * 16: Introduction * 17: A.C. Harvey and C. Fernandes: Times Series Models for Count Data or Qualitative Observations * 18: Carter and Kohn: On Gibbs Sampling for State Space Models * 19: P. de Jong and N. Shephard: The Simulation Smoother * 20: N. Shephard and M.K. Pitt: Likelihood Analysis of Non-Gaussian Measurement Time Series * 21: J. Durbin and S.J. Koopman: Time Series Analysis of Non-Gaussian Observations based on State Space Models from both Classical and Bayesian Perspectives * 22: S. Kim, N. Shephard, and S. Chib: Stochastic Volatility: Liklihood Inference and Comparison with ARCH Models * 23: A. Doucet, S.J. Godsill, and C. Andrieu: On Sequential Monte Carlo Sampling Methods for Bayesian Filtering
* Signal Extraction and Likelihood Inference for Linear UC Models * 1: Introduction * 2: P. Burridge and K.F. Wallis: Prediction Theory for Autoregressive-Moving Average Processes * 3: S.J. Koopman: Exact Initial Kalman Filtering and Smoothing for Non-stationary Time Series Models * 4: P. de Jong: Smoothing and Interpolation with the State Space Model * 5: A.C. Harvey and S.J. Koopman: Diagnostic Checking of Unobserved Components in Time Series Models * 6: R. Kohn, C.F. Ansley and C. Wong: Nonparametric Spline Regression with Autoregressive Moving Average Errors * Unobserved Components in Economic Time Series * 7: Introduction * 8: M.W. Watson: Univariate Detrending Methods with Stochastic Trends * 9: A.C. Harvey and A. Jaeger: Detrending, Stylized Facts and the Business Cycle * 10: A. Maravall: Stochastic Linear Trends, Models and Estimators * 11: D. Pfeffermann: Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys * 12: A.C. Harvey, S.J. Koopman and M. Riani: The Modelling and Seasonal Adjustment of Weekly Observations * Testing in Unobserved Components Models * 13: Introduction * 14: J. Nyblom: Testing for Deterministic Linear Trends in a Times Series * 15: F. Canova and B.E. Hansen: Are Seasonal Patterns Stable Over Time? A Test for Seasonal Stability * Non-Linear and Non- Gaussian Models * 16: Introduction * 17: A.C. Harvey and C. Fernandes: Times Series Models for Count Data or Qualitative Observations * 18: Carter and Kohn: On Gibbs Sampling for State Space Models * 19: P. de Jong and N. Shephard: The Simulation Smoother * 20: N. Shephard and M.K. Pitt: Likelihood Analysis of Non-Gaussian Measurement Time Series * 21: J. Durbin and S.J. Koopman: Time Series Analysis of Non-Gaussian Observations based on State Space Models from both Classical and Bayesian Perspectives * 22: S. Kim, N. Shephard, and S. Chib: Stochastic Volatility: Liklihood Inference and Comparison with ARCH Models * 23: A. Doucet, S.J. Godsill, and C. Andrieu: On Sequential Monte Carlo Sampling Methods for Bayesian Filtering
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