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Book Categories |
Pt. I | State space models | 1 |
1 | Introduction to state space time series analysis | 3 |
2 | State structure, decision making and related issues | 26 |
3 | An introduction to particle filters | 40 |
Pt. II | Testing | 73 |
4 | Frequency domain and wavelet-based estimation for long-memory signal plus noise models | 75 |
5 | A goodness-of-fit test for AR(1) models and power against state space alternatives | 92 |
6 | Tests for cycles | 102 |
Pt. III | Bayesian inference and bootstrap | 121 |
7 | Efficient Bayesian parameter estimation | 123 |
8 | Empirical Bayesian inference in a nonparametric regression model | 152 |
9 | Resampling in state space models | 171 |
Pt. IV | Applications | 203 |
10 | Measuring and forecasting financial variability using realised variance | 205 |
11 | Practical filtering for stochastic volatility models | 236 |
12 | On RegComponent time series models and their applications | 248 |
13 | State spaces modelling in macroeconomics and finance using SsfPack in S+Finmetrics | 284 |
14 | Finding genes in the human genome with hidden Markov models | 336 |
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