包邮Time series analysis and its applications with R examples

Preface to the Fourth Edition Preface to the Third Edition 1 Characteristics of Time Series 1．1 The Nature of Time Series Data 1．2 Time Series Statistical Models 1．3 Measures of Dependence 1．4 Stationary Time Series 1．5 Estimation of Correlation 1．6 Vector-Valued and Multidimensional Series Problems 2 Time Series Regression and Exploratory Data Analysis 2．1 Classical Regression in the Time Series Context 2．2 Exploratory Data Analysis 2．3 Smoothing in the Time Series Context Problems 3 ARIMA Models 3．1 Autoregressive Moving Average Models 3．2 Difference Equations 3．3 Autocorrelation and Partial Autocorrelation 3．4 Forecasting 3．5 Estimation 3．6 Integrated Models for Nonstationary Data 3．7 Building ARIMA Models 3．8 Regression with Autocorrelated Errors 3．9 Multiplicative Seasonal ARIMA Models Problems 4 Spectral Analysis and Filtering 4．1 Cyclical Behavior and Periodicity 4．2 The Spectral Density 4．3 Periodogram and Discrete Fourier Transform 4．4 Nonparametric Spectral Estimation 4．5 Parametric Spectral Estimation 4．6 Multiple Series and Cross-Spectra 4．7 Linear Filters 4．8 Lagged Regression Models 4．9 Signal Extraction and Optimum Filtering 4．10 Spectral Analysis of Multidimensional Series Problems 5 Additional Time Domain Topics 5．1 Long Memory ARMA and Fractional Differencing 5．2 Unit Root Testing 5．3 GARCH Models 5．4 Threshold Models 5．5 Lagged Regression and Transfer Function Modeling 5．6 Multivariate ARMAX Models Problems 6 State Space Models 6．1 Linear Gaussian Model 6．2 Filtering， Smoothing，and Forecasting 6．3 Maximum Likelihood Estimation 6．4 Missing Data Modifications 6．5 Structural Models： Signal Extraction and Forecasting 6．6 State-Space Models with Correlated Errors 6．6．1 ARMAX Models 6．6．2 Multivariate Regression with Autocorrelated Errors 6．7 Bootstrapping State Space Models 6．8 Smoothing Splines and the Kalman Smoother 6．9 Hidden Markov Models and Switching Aut oregression 6．10 Dynamic Linear Models with Switching 6．1．1 Stochastic Volatility 6．1．2 Bayesian Analysis of State Space Models Problems 7 Statistical Methods in the Frequency Domain 7．1 Introduction 7．2 Spectral Matrices and Likelihood Functions 7．3 Regression for Joindy Stationary Series 7．4 Regression with Deterministic Inputs 7．5 Random Coefficient Regression …… Appendix A Large Sample Theory Appendix B Time Domain Theory Appendix C SpectraIDomain Theory Appendix R R Supplement References Index