ABSTRACT

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian t

chapter 1|32 pages

Notation, definitions, and basic inference

chapter 2|52 pages

Traditional time domain models

chapter 3|32 pages

The frequency domain

chapter 4|28 pages

Dynamic linear models

chapter 5|16 pages

State-space TVAR models

chapter 6|28 pages

SMC methods for state-space models

chapter 7|42 pages

Mixture models in time series

chapter 9|12 pages

Vector AR and ARMA models

chapter 10|58 pages

Multivariate DLMs and covariance models