Dynamic Linear Models with R

State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, Dynamic Linear Models with R focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used.

A final chapter covers modern sequential Monte Carlo algorithms. Dynamic Linear Models with R illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in Dynamic Linear Models with R is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.

- Introduction: Basic notions about Bayesian inference
- Dynamic linear models
- Model classification
- Models with unknown parameters
- Sequential Monte Carlo methods