State Space and Unobserved Component Models

Offering a broad overview of the state-of-the-art developments in the theory and applications of state space modeling, fourteen chapters from twenty-three contributors present a unique synthesis of state space methods and unobserved component models important in a wide range of subjects. They include economics, finance, environmental science, medicine and engineering. A useful reference for all researchers and students who use state space methodology, this accessible volume makes a significant contribution to the advancement of the discipline.

State Space Models
Introduction to state space time series analysis
State structure, decision making and related issues
An introduction to particle filters
Testing
Frequence domain and wavelet-based estimation for long-memory signal plus noise models
A goodness-of-fit test for AR (1) models and power against state-space alternatives
Test for cycles
Bayesian Inference and Bootstrap
Efficient Bayesian parameter estimation
Empirical Bayesian inference in a nonparametric regression model
Resampling in state space models
Applications
Measuring and forecasting financial variability using realised variance
Practical filtering for stochastic volatility models
On RegComponent time series models and their applications
State space modeling in macroeconomics and finance using SsfPack in S+Finmetrics
Finding genes in the human genome with hidden Markov models
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State Space and Unobserved Component Models

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