Time series analysis (Q2803795)

This book is intended as an introductory text on the subject of time series. It focuses on methodologies, techniques and examples rather than theoretical results. The contents of the chapters are as follows:NEWLINENEWLINEChapter 1 gives an overview of time series modeling: stationary vs. non-stationary, parametric vs. nonparametric, whiteness tests;NEWLINENEWLINEChapter 2 is on linear processes: ARMA models, long memory models;NEWLINENEWLINEChapter 3 discusses state space models;NEWLINENEWLINEChapter 4 discusses the analysis of time series in frequency domain;NEWLINENEWLINEChapter 5 gives an overview of estimation of time series models, providing discussion on maximum likelihood, Whittle approach, Bayesian estimation;NEWLINENEWLINEChapter 6 addresses nonlinear models: ARCH, stochastic volatility, TAR models;NEWLINENEWLINEChapter 7 has its main topics on forecasting time series, methods applied to linear and heteroskedastic models;NEWLINENEWLINEChapter 8 discusses non-stationary processes: trends, stochastic trends, unit roots, ARIMA models and locally stationary processes;NEWLINENEWLINEChapter 9 presents methods for analyzing time series with seasonal patterns;NEWLINENEWLINEChapter 10 reviews time series regression methods, including polynomial and harmonic regression;NEWLINENEWLINEChapter 11 examines the effects of missing values and outliers in the analysis of time series;NEWLINENEWLINEChapter 12 treats non-Gaussian time series: INAR models, conditional distribution models, parameter driven models.NEWLINENEWLINEAn appendix with more theoretical results and solutions of selected problems complete the book.