【簡介】 A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA. Providing contemporary coverage, the book features several useful and newlydeveloped techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models. Time Series Analysis includes practical applications of time series methods throughout, as well as: Real-world examples and exercise sets that allow readers to practice the presented methods and techniques Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time End-of-chapter proposed problems and bibliographical notes to deepen readers’ knowledge of the presented material Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout A companion website with additional data fi les and computer codes Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance. Wilfredo Palma, PhD, is Professor of Statistics in the Department of Statistics at Pontificia Universidad Católica de Chile. He has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics. He is the author of Long-Memory Time Series: Theory and Methods, also published by Wiley. 【目錄】 Preface xiii Acknowledgments xvii Acronyms xix 1 Introduction 1 1.1 Time Series Data 2 1.2 Random Variables and Statistical Modeling 16 1.3 Discrete-Time Models 22 1.4 Serial Dependence 22 1.5 Nonstationarity 25 1.6 Whiteness Testing 32 1.7 Parametric and Nonparametric Modeling 36 1.8 Forecasting 38 1.9 Time Series Modeling 38 1.10 Bibliographic Notes 39 Problems 39 2 Linear Processes 43 2.1 Definition 44 2.2 Stationarity 44 2.3 Invertibility 45 2.4 Causality 46 2.5 Representations of Linear Processes 46 2.6 Weak and Strong Dependence 49 2.7 ARMA Models 51 2.8 Autocovariance Function 56 2.9 ACF and Partial ACF Functions 57 2.10 ARFIMA Processes 64 2.11 Fractional Gaussian Noise 71 2.12 Bibliographic Notes 72 Problems 72 3 State Space Models 89 3.1 Introduction 90 3.2 Linear Dynamical Systems 92 3.3 State space Modeling of Linear Processes 96 3.4 State Estimation 97 3.5 Exogenous Variables 113 3.6 Bibliographic Notes 114 Problems 114 4 Spectral Analysis 121 4.1 Time and Frequency Domains 122 4.2 Linear Filters 122 4.3 Spectral Density 123 4.4 Periodogram 125 4.5 Smoothed Periodogram 128 4.6 Examples 130 4.7 Wavelets 136 4.8 Spectral Representation 138 4.9 Time-Varying Spectrum 140 4.10 Bibliographic Notes 145 Problems 145 5 Estimation Methods 151 5.1 Model Building 152 5.2 Parsimony 152 5.3 Akaike and Schwartz Information Criteria 153 5.4 Estimation of the Mean 153 5.5 Estimation of Autocovariances 154 5.6 Moment Estimation 155 5.7 Maximum-Likelihood Estimation 156 5.8 Whittle Estimation 157 5.9 State Space Estimation 160 5.10 Estimation of Long-Memory Processes 161 5.11 Numerical Experiments 178 5.12 Bayesian Estimation 180 5.13 Statistical Inference 184 5.14 Illustrations 189 5.15 Bibliographic Notes 193 Problems 194 6 Nonlinear Time Series 209 6.1 Introduction 210 6.2 Testing for Linearity 211 6.3 Heteroskedastic Data 212 6.4 ARCH Models 213 6.5 GARCH Models 216 6.6 ARFIMA-GARCH Models 218 6.7 ARCH(1) Models 220 6.8 APARCH Models 222 6.9 Stochastic Volatility 222 6.10 Numerical Experiments 223 6.11 Data Applications 225 6.12 Value at Risk 236 6.13 Autocorrelation of Squares 241 6.14 Threshold autoregressive models 247 6.15 Bibliographic Notes 252 Problems 253 7 Prediction 267 7.1 Optimal Prediction 268 7.2 One-Step Ahead Predictors 268 7.3 Multistep Ahead Predictors 275 7.4 Heteroskedastic Models 276 7.5 Prediction Bands 281 7.6 Data Application 287 7.7 Bibliographic Notes 289 Problems 289 8 Nonstationary Processes 295 8.1 Introduction 296 8.2 Unit Root Testing 297 8.3 ARIMA Processes 298 8.4 Locally Stationary Processes 301 8.5 Structural Breaks 326 8.6 Bibliographic Notes 331 Problems 332 9 Seasonality 337 9.1 SARIMA Models 338 9.2 SARFIMA Models 351 9.3 GARMA Models 353 9.4 Calculation of the Asymptotic Variance 355 9.5 Autocovariance Function 355 9.6 Monte Carlo Studies 359 9.7 Illustration 362 9.8 Bibliographic Notes 364 Problems 365 10 Time Series Regression 369 10.1 Motivation 370 10.2 Definitions 373 10.3 Properties of the LSE 375 10.4 Properties of the BLUE 376 10.5 Estimation of the Mean 379 10.6 Polynomial Trend 382 10.7 Harmonic Regression 386 10.8 Illustration: Air Pollution Data 388 10.9 Bibliographic Notes 392 Problems 392 11 Missing Values and Outliers 399 11.1 Introduction 400 11.2 Likelihood Function with Missing Values 401 11.3 Effects of Missing Values on ML Estimates 405 11.4 Effects of Missing Values on Prediction 407 11.5 Interpolation of Missing Data 410 11.6 Spectral Estimation with Missing Values 418 11.7 Outliers and Intervention Analysis 421 11.8 Bibliographic Notes 434 Problems 435 12 Non-Gaussian Time Series 441 12.1 Data Driven Models 442 12.2 Parameter Driven Models 452 12.3 Estimation 453 12.4 Data Illustrations 466 12.5 Zero-Inflated Models 477 12.6 Bibliographic Notes 483 Problems 483 Appendix A: Complements 487 A.1 Projection Theorem 488 A.2 Wold Decomposition 490 A.3 Bibliographic Notes 497 Appendix B: Solutions to Selected Problems 499 Appendix C: Data and Codes 557 References 559 Topic Index 573 Author Index 577