Nonlinear Time Series: Theory, Methods and Applications with R Examples

Features
Describes the major statistical techniques for inferring model parameters, with a focus on the MLE and QMLE
Introduces concepts of nonparametric statistics, including smoothing splines
Covers HMM models, including Gaussian linear, switching Markovian, and nonlinear state space models
Present direct likelihood inference techniques and the EM algorithm
Uses R for numerical examples and provides a dedicated R package
Solutions manual available upon qualifying course adoption
This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time series models as well as several complementary approaches. They discuss the main limit theorems for Markov chains, useful inequalities, statistical techniques to infer model parameters, and GLMs. Moving on to HMM models, the book examines filtering and smoothing, parametric and nonparametric inference, advanced particle filtering, and numerical methods for inference.