义和团运动史料丛编第一辑

Main subject categories: • Time series analysis • Econometrics • Dynamical systems and ergodic theory • Inference from stochastic processes • Statistics • Game theory, economics, finance, and other social and behavioral sciencesThis new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis. The book covers a wide range of topics, including ARIMA models, forecasting methods, spectral analysis, linear systems, state-space models, the Kalman filters, nonlinear models, volatility models, and multivariate models.Highlights of the seventh edition: • A new chapter on univariate volatility models • A revised chapter on linear time series models • A new section on multivariate volatility models • A new section on regime switching models • Many new worked examples, with R code integrated into the textThe book can be used as a textbook for an undergraduate or a graduate level time series course in statistics. The book does not assume many prerequisites in probability and statistics, so it is also intended for students and data analysts in engineering, economics, and finance. Preface to the Seventh Edition Abbreviations and Notation 1 Introduction 1.1 Some Representative Time Series 1.2 Terminology 1.3 Objectives of Time Series Analysis 1.4 Approaches to Time Series Analysis 1.5 Review of Books on Time Series 2 Basic Descriptive Techniques 2.1 Types of Variation 2.2 Stationary Time Series 2.3 The Time Plot 2.4 Transformations 2.5 Analysing Series that Contain a Trend and No Seasonal Variation 2.5.1 Curve Fitting 2.5.2 Filtering 2.5.3 Differencing 2.5.4 Other approaches 2.6 Analysing Series that Contain a Trend and Seasonal Variation 2.7 Autocorrelation and the Correlogram 2.7.1 The correlogram 2.7.2 Interpreting the correlogram 2.8 Other Tests of Randomness 2.9 Handling Real Data 3 Some Linear Time Series Models 3.1 Stochastic Processes and Their Properties 3.2 Stationary Processes 3.3 Properties of the Autocorrelation Function 3.4 Purely Random Processes 3.5 Random Walks 3.6 Moving Average Processes 3.6.1 Stationarity and autocorrelation function of an MA process 3.6.2 Invertibility of an MA process 3.7 Autoregressive Processes 3.7.1 First-order process 3.7.2 General-order process 3.8 Mixed ARMA Models 3.8.1 Stationarity and invertibility conditions 3.8.2 Yule-Walker equations and autocorrelations 3.8.3 AR and MA representations 3.9 Integrated ARMA (or ARIMA) Models 3.10 Fractional Differencing and Long-Memory Models 3.11 The General Linear Process 3.12 Continuous Processes 3.13 The Wold Decomposition Theorem 4 Fitting Time Series Models in the Time Domain 4.1 Estimating Autocovariance and Autocorrelation Functions 4.1.1 Using the correlogram in modelling 4.1.2 Estimating the mean 4.1.3 Ergodicity 4.2 Fitting an Autoregressive Process 4.2.1 Estimating parameters of an AR process 4.2.2 Determining the order of an AR process 4.3 Fitting a Moving Average Process 4.3.1 Estimating parameters of an MA process 4.3.2 Determining the order of an MA process 4.4 Estimating Parameters of an ARMA Model 4.5 Model Identification Tools 4.6 Testing for Unit Roots 4.7 Estimating Parameters of an ARIMA Model 4.8 Box{Jenkins Seasonal ARIMA Models 4.9 Residual Analysis 4.10 General Remarks on Model Building 5 Forecasting 5.1 Introduction 5.2 Extrapolation and Exponential Smoothing 5.2.1 Extrapolation of trend curves 5.2.2 Simple exponential smoothing 5.2.3 The Holt and Holt{Winters forecasting procedures 5.3 The Box{Jenkins Methodology 5.3.1 The Box-Jenkins procedures 5.3.2 Other methods 5.3.3 Prediction intervals 5.4 Multivariate Procedures 5.4.1 Multiple regression 5.4.2 Econometric models 5.4.3 Other multivariate models 5.5 Comparative Review of Forecasting Procedures 5.5.1 Forecasting competitions 5.5.2 Choosing a non-automatic method 5.5.3 A strategy for non-automatic univariate forecasting 5.5.4 Summary 5.6 Prediction Theory 6 Stationary Processes in the Frequency Domain 6.1 Introduction 6.2 The Spectral Distribution Function 6.3 The Spectral Density Function 6.4 The Spectrum of a Continuous Process 6.5 Derivation of Selected Spectra 7 Spectral Analysis 7.1 Fourier Analysis 7.2 A Simple Sinusoidal Model 7.3 Periodogram Analysis 7.3.1 The relationship between the periodogram and the autocovariance function 7.3.2 Properties of the periodogram 7.4 Some Consistent Estimation Procedures 7.4.1 Transforming the truncated autocovariance function 7.4.2 Hanning 7.4.3 Hamming 7.4.4 Smoothing the periodogram 7.4.5 The fast Fourier transform (FFT) 7.5 Confidence Intervals for the Spectrum 7.6 Comparison of Different Estimation Procedures 7.7 Analysing a Continuous Time Series 7.8 Examples and Discussion 8 Bivariate Processes 8.1 Cross-Covariance and Cross-Correlation 8.1.1 Examples 8.1.2 Estimation 8.1.3 Interpretation 8.2 The Cross-Spectrum 8.2.1 Examples 8.2.2 Estimation 8.2.3 Interpretation 9 Linear Systems 9.1 Introduction 9.2 Linear Systems in the Time Domain 9.2.1 Some types of linear systems 9.2.2 The impulse response function: An explanation 9.2.3 The step response function 9.3 Linear Systems in the Frequency Domain 9.3.1 The frequency response function 9.3.2 Gain and phase diagrams 9.3.3 Some examples 9.3.4 General relation between input and output 9.3.5 Linear systems in series 9.3.6 Design of Filters 9.4 Identification of Linear Systems 9.4.1 Estimating the frequency response function 9.4.2 The Box{Jenkins approach 9.4.3 Systems involving feedback 10 State-Space Models and the Kalman Filter 10.1 State-Space Models 10.1.1 The random walk plus noise model 10.1.2 The linear growth model 10.1.3 The basic structural model 10.1.4 State-space representation of an AR(2) process 10.1.5 Bayesian forecasting 10.1.6 A regression model with time-varying coefficients 10.1.7 Model building 10.2 The Kalman Filter 11 Non-Linear Models 11.1 Introduction 11.1.1 Why non-linearity? 11.1.2 What is a linear model? 11.1.3 What is a non-linear model? 11.1.4 What is white noise? 11.2 Non-Linear Autoregressive Processes 11.3 Threshold Autoregressive Models 11.4 Smooth Transition Autoregressive Models 11.5 Bilinear Models 11.6 Regime-Switching Models 11.7 Neural Networks 11.8 Chaos 11.9 Concluding Remarks 11.10 Bibliography 12 Volatility Models 12.1 Structure of a Model for Asset Returns 12.2 Historic Volatility 12.3 Autoregressive Conditional Heteroskedastic (ARCH) Models 12.4 Generalized ARCH Models 12.5 The ARMA-GARCH Models 12.6 Other ARCH-Type Models 12.6.1 The integrated GARCH model 12.6.2 The exponential GARCH model 12.7 Stochastic Volatility Models 12.8 Bibliography 13 Multivariate Time Series Modelling 13.1 Introduction 13.1.1 One equation or many? 13.1.2 The cross-correlation function 13.1.3 Initial data analysis 13.2 Single Equation Models 13.3 Vector Autoregressive Models 13.3.1 VAR(1) models 13.3.2 VAR(p) models 13.4 Vector ARMA Models 13.5 Fitting VAR and VARMA Models 13.6 Co-Integration 13.7 Multivariate Volatility Models 13.7.1 Exponentially weighted estimate 13.7.2 BEKK models 13.8 Bibliography 14 Some More Advanced Topics 14.1 Modelling Non-Stationary Time Series 14.2 Model Uncertainty 14.3 Control Theory 14.4 Miscellanea 14.4.1 Autoregressive spectrum estimation 14.4.2 Wavelets 14.4.3 `Crossing' problems 14.4.4 Observations at unequal intervals, including missing values 14.4.5 Outliers and robust methods 14.4.6 Repeated measurements 14.4.7 Aggregation of time series 14.4.8 Spatial and spatio-temporal series 14.4.9 Time series in Finance 14.4.10 Discrete-valued time series Appendix A Fourier, Laplace, and z-Transforms Appendix B Dirac Delta Function Appendix C Covariance and Correlation Answers to Exercises References Index This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis. The book covers a wide range of topics, including ARIMA models, forecasting methods, spectral analysis, linear systems, state-space models, the Kalman filters, nonlinear models, volatility models, and multivariate models. It also presents many examples and implementations of time series models and methods to reflect advances in the field. Highlights of the seventh edition: — A new chapter on univariate volatility models — A revised chapter on linear time series models — A new section on multivariate volatility models — A new section on regime switching models — Many new worked examples, with R code integrated into the text The book can be used as a textbook for an undergraduate or a graduate level time series course in statistics. The book does not assume many prerequisites in probability and statistics, so it is also intended for students and data analysts in engineering, economics, and finance. "Since 1975, The Analysis of Time Series: An Introduction has introduced legions of statistics students and researchers to the theory and practice of time series analysis. The sixth edition provides an accessible, comprehensive introduction to the theory and practice of time series analysis. The treatment covers a wide range of topics, including ARIMA probability models, forecasting methods, spectral analysis, linear systems, state-space models, and the Kalman filter. It also addresses nonlinear, multivariate, and long-memory models. The author has carefully updated each chapter, added new discussions, incorporated new datasets, and made those datasets available at www.crcpress.com."--Jacket.