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

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

• SIGNAL EXTRACTION AND LIKELIHOOD INFERENCE FOR LINEAR UC MODELS ; 1. Introduction ; 2. Prediction Theory for Autoregressive-Moving Average Processes ; 3. Exact Initial Kalman Filtering and Smoothing for Non-stationary Time Series Models ; 4. Smoothing and Interpolation with the State Space Model ; 5. Diagnostic Checking of Unobserved Components in Time Series Models ; 6. Nonparametric Spline Regression with Autoregressive Moving Average Errors ; UNOBSERVED COMPONENTS IN ECONOMIC TIME SERIES ; 7. Introduction ; 8. Univariate Detrending Methods with Stochastic Trends ; 9. Detrending, Stylized Facts and the Business Cycle ; 10. Stochastic Linear Trends, Models and Estimators ; 11. Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys ; 12. The Modelling and Seasonal Adjustment of Weekly Observations ; TESTING IN UNOBSERVED COMPONENTS MODELS ; 13. Introduction ; 14. Testing for Deterministic Linear Trends in a Times Series ; 15. Are Seasonal Patterns Stable Over Time? A Test for Seasonal Stability ; NON-LINEAR AND NON- GAUSSIAN MODELS ; 16. Introduction ; 17. Times Series Models for Count Data or Qualitative Observations ; 18. On Gibbs Sampling for State Space Models ; 19. The Simulation Smoother ; 20. Likelihood Analysis of Non-Gaussian Measurement Time Series ; 21. Time Series Analysis of Non-Gaussian Observations based on State Space Models from both Classical and Bayesian Perspectives ; 22. Stochastic Volatility: Liklihood Inference and Comparison with ARCH Models ; 23. On Sequential Monte Carlo Sampling Methods for Bayesian Filtering

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

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

• SIGNAL EXTRACTION AND LIKELIHOOD INFERENCE FOR LINEAR UC MODELS ; 1. Introduction ; 2. Prediction Theory for Autoregressive-Moving Average Processes ; 3. Exact Initial Kalman Filtering and Smoothing for Non-stationary Time Series Models ; 4. Smoothing and Interpolation with the State Space Model ; 5. Diagnostic Checking of Unobserved Components in Time Series Models ; 6. Nonparametric Spline Regression with Autoregressive Moving Average Errors ; UNOBSERVED COMPONENTS IN ECONOMIC TIME SERIES ; 7. Introduction ; 8. Univariate Detrending Methods with Stochastic Trends ; 9. Detrending, Stylized Facts and the Business Cycle ; 10. Stochastic Linear Trends, Models and Estimators ; 11. Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys ; 12. The Modelling and Seasonal Adjustment of Weekly Observations ; TESTING IN UNOBSERVED COMPONENTS MODELS ; 13. Introduction ; 14. Testing for Deterministic Linear Trends in a Times Series ; 15. Are Seasonal Patterns Stable Over Time? A Test for Seasonal Stability ; NON-LINEAR AND NON- GAUSSIAN MODELS ; 16. Introduction ; 17. Times Series Models for Count Data or Qualitative Observations ; 18. On Gibbs Sampling for State Space Models ; 19. The Simulation Smoother ; 20. Likelihood Analysis of Non-Gaussian Measurement Time Series ; 21. Time Series Analysis of Non-Gaussian Observations based on State Space Models from both Classical and Bayesian Perspectives ; 22. Stochastic Volatility: Liklihood Inference and Comparison with ARCH Models ; 23. On Sequential Monte Carlo Sampling Methods for Bayesian Filtering