非線性時(shí)間序列

內(nèi)容概要

本書論述當(dāng)代統(tǒng)計(jì)方法和非線性時(shí)間序列分析，著重闡述過(guò)去十年發(fā)展起來(lái)的非參數(shù)和半?yún)?shù)技術(shù)。主要內(nèi)容包括相空間、頻域及時(shí)域中的建模技術(shù)；為說(shuō)明參數(shù)方法和非參數(shù)方法在時(shí)間序列數(shù)據(jù)分析中的一體性，本書給出某些參數(shù)化非線性模型的最新論述，如ARCH／GARCH模型和閾值模型；以及關(guān)于ARMA模型的一個(gè)簡(jiǎn)潔觀點(diǎn)。本書始終使用實(shí)際應(yīng)用中得到的數(shù)據(jù)，闡明如何借助非參數(shù)方法揭示高維數(shù)據(jù)的局部結(jié)構(gòu)。本書還介紹了一些重要的技術(shù)工具。    本書適合研究生，時(shí)間序列分析方面的實(shí)際工作者，該領(lǐng)域不同程度的研究人員。本書在統(tǒng)計(jì)界和諸如計(jì)量經(jīng)濟(jì)學(xué)、實(shí)證金融學(xué)、群體生物學(xué)及生態(tài)學(xué)之類的其他廣泛領(lǐng)域都有其價(jià)值。閱讀本書需要概率論和統(tǒng)計(jì)的基本知識(shí)。

書籍目錄

Preface1 Introduction  1.1 Examples of Times Series  1.2 Objectives of Time Series Analysis  1.3 Linear Time Series Models  1.4 What Is a Nonlinear Time Series?  1.5 Nonlinear Time Series Models  1.6 From Linear to Nonlinear Modes  1.7 Further Reading  1.8 Software Implementations2 Characteristics of Time Series  2.1 Stationarity  2.2 Autocorrelation  2.3 Spectral Distributions  2.4 Periodogram  2.5 Long-Memory Processes  2.6 Mixing  2.7 Complements  2.8 Additional bibliographical Notes3 ARMA Modeling and Forecasting  3.1 Models and Background  3.2 The Best Linear Prediction---Prewhitening  3.3 Maximum Likelihood Estimation  3.4 Order Determination  3.5 Diagnostic Checking  3.6 A Real Data Example---Analyzing German Egg Prices  3.7 Linear Forecasting4 Parametric Nonlinear Time Series Modes  4.1 Threshold Models  4.2 ARCH and GARCh Models  4.3 Bilinear Models  4.4 Additional Bibliographical notes5 Nonparametric Density Estimation  5.1 Introduction  5.2 Kernel Density Estimation  5.3 Windowing and Whitening  5.4 Bandwidth Selection  5.5 boundary Correction  5.6 Asymptotic Results  5.7 Complements---Proof of Theorem 5.3  5.8 Bibliographical Notes6 Smoothing in Time Series  6.1 Introduction  6.2 Smoothing in the Time Domain  6.3 Smoothing in the State Domain  6.4 Spline Methods  6.5 Estimation of Conditional Densities  6.6 Complements  6.7 Bibliographical Notes7 Spectral Density Estimation and Its Applications  7.1 Introduction  7.2 Tapering, Kernel Estimation, and Prewhitening  7.3 Automatic Estimation of Spectral Density  7.4 Tests for White Noise  7.5 Complements  7.6 bibliographical Notes8 Nonparametric Models  8.1 Introduction  8.2 Multivatriate Local Polynomial Regression  8.3 Functional-Coefficient Autoregressive Model  8.4 Adaptive Functional-Coefficient Autoregressive Models  8.5 Additive Models  8.6 Other Nonparametric Models  8.7 Modeling Conditional Variance  8.8 Complements  8.9 Bibliographical Notes9 Model Validation  9.1 Introduction  9.2 Generalized Likelihood Ration Tests  9.3 Tests on Spectral Densities  9.4 Autoregressive versus Nonparametric Models  9.5 Threshold Models versus Varying-Coefficient Models  9.6 Bibliographical Notes10 Nonlinear Prediction  10.1 Features of Nonlinear Prediction  10.2 Point Prediction  10.3 Estimating Predictive Distributions  10.4 Interval Predictors and Predictive Sets  10.5 Complements  10.6 Additional Bibliographical NotesReferencesAuthor indexSubject index

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