時(shí)間序列與預(yù)測(cè)

前言

This book is aimed at the reader who wishes to gain a working knowledge of timeseries and forecasting methods as applied in economics, engineering and the naturaland social sciences. Unlike our earlier book, Time Series: Theory and Methods, re-ferred to in the text as TSTM, this one requires only a knowledge of basic calculus,matrix algebra and elementary statistics at the level （for example） of Mendenhall,Wackerly and Scheaffer （1990）. It is intended for upper-level undergraduate studentsand beginning graduate students.The emphasis is on methods and the analysis of data sets. The student versionof the time series package ITSM2000, enabling the reader to reproduce most of thecalculations in the text （and to analyze further data sets of the reader's own choosing）,is included on the CD-ROM~~which accompanies the book. The data sets used in thebook are also included. The package requires an IBM-compatible PC operating underWindows 95, NT version 4.0, or a later version of either of these operating systems.The program ITSM can be run directly from the CD-ROM or installed on a hard diskas described at the beginning of Appendix D, where a detailed introduction to thepackage is provided.Very little prior familiarity with computing is required in order to use the computerpackage. Detailed instructions for its use are found in the on-line help files whichare accessed, when the program ITSM is running, by selecting the menu optionHelp>Contents and selecting the topic of interest. Under the heading Data youwill find information concerning the data sets stored on the CD-ROM. The book canalso be used in conjunction with other computer packages for handling time series.Chapter 14 of the book by Venables and Ripley （1994） describes how to performmany of the calculations using S-plus.There are numerous problems at the end of each chapter, many of which involveuse of the programs to study the data sets provided.To make the underlying theory accessible to a wider audience, we have statedsome of the key mathematical results without proof, but have attempted to ensurethat the logical structure of the development is otherwise complete. （References toproofs are provided for the interested reader.）

內(nèi)容概要

　　《時(shí)間序列與預(yù)測(cè)(英文版)(第2版)》是時(shí)間序列領(lǐng)域的名著。特色在于注重實(shí)際應(yīng)用。深淺適中，適用面廣，示例和習(xí)題豐富，有微積分、線性代數(shù)和統(tǒng)計(jì)學(xué)基礎(chǔ)知識(shí)即可閱讀。書(shū)中全面介紹了經(jīng)濟(jì)、工程、自然科學(xué)和社會(huì)科學(xué)中所用的時(shí)間序列和預(yù)測(cè)方法，核心內(nèi)容是平穩(wěn)過(guò)程、ARMA模型和ARIMA模型、多元時(shí)間序列和狀態(tài)空間模型、譜分析。書(shū)中配有時(shí)間序列軟件包ITSM2000學(xué)生版，更加方便讀者學(xué)習(xí)。

作者簡(jiǎn)介

作者：(美國(guó))Peter J.Brockwell (美國(guó))Richard A.DavisPeter J．Brockwell  世界著名統(tǒng)計(jì)學(xué)家。ASA(美國(guó)統(tǒng)計(jì)協(xié)會(huì))、IMS（數(shù)理統(tǒng)計(jì)學(xué)會(huì))會(huì)士。科羅拉多州立大學(xué)統(tǒng)計(jì)系榮休教授。他是Journalof Time Series Analysis副主編，并Li Richard A．Davis合作開(kāi)發(fā)了時(shí)間序列軟件包ITSM2000。Richard A．Davis 世界著名統(tǒng)計(jì)學(xué)家。ASA(美國(guó)統(tǒng)計(jì)協(xié)會(huì))、IMS(數(shù)理統(tǒng)計(jì)學(xué)會(huì))會(huì)士?？屏_拉多州立大學(xué)統(tǒng)計(jì)系教授，1997年至2005年擔(dān)任該系的系主任。1 998年榮獲計(jì)量經(jīng)濟(jì)學(xué)Koopmans獎(jiǎng)。他是Stochastic Processes and Their Applications，Annals of Applied Probability等期刊編委，是Proceedings ofthe American Mathematics Society的統(tǒng)計(jì)學(xué)領(lǐng)域主編。

書(shū)籍目錄

1. Introduction1.1. Examples of Time Series1.2. Objectives of Time Series Analysis1.3. Some Simple Time Series Models1.3.1. Some Zero-Mean Models1.3.2. Models with Trend and Seasonality1.3.3. A General Approach to Time Series Modeling1.4. Stationary Models and the Autocorrelation Function1.4.1. The Sample Autocorrelation Function1.4.2. A Model for the Lake Huron Data1.5. Estimation and Elimination of Trend and Seasonal Components1.5.1. Estimation and Elimination of Trend in the Absence ofSeasonality1.5.2. Estimation and Elimination of Both Trend andSeasonality1.6. Testing the Estimated Noise SequenceProblems2. Stationary Processes2.1. Basic Properties2.2. Linear Processes2.3. Introduction to ARMA Processes2.4. Properties of the Sample Mean and Autocorrelation Function2.4.1. Estimation of tz2.4.2. Estimation of y(.) and p(.)2.5. Forecasting Stationary Time Series2.5.1. The Durbin-Levinson Algorithm2.5.2. The Innovations Algorithm2.5.3. Prediction of a Stationary Process in Terms of InfinitelyMany Past Values2.6. The Wold DecompositionProblems3. ARMA Models3.1. ARMA(p, q) Processes3.2. The ACF and PACF of an ARMA(p, q) Process3.2.1. Calculation of the ACVF3.2.2. The Autocorrelation Function3.2.3. The Partial Autocorrelation Function3.2.4. Examples3.3. Forecasting ARMA ProcessesProblems4. Spectral Analysis4.1. Spectral Densities4.2. The Periodogram4.3. Time-Invariant Linear Filters4.4. The Spectral Density of an ARMA ProcessProblems5. Modeling and Forecasting with ARMA Processes5. I. Preliminary Estimation5.1.1. Yule-Walker Estimation5.1.2. Burg's Algorithm5.1.3. The Innovations Algorithm5.1.4. The Hannan-Rissanen Algorithm5.2. Maximum Likelihood Estimation5.3. Diagnostic Checking5.3.1. The Graph of5.3.2. The Sample ACF of the Residuals5.3.3. Tests for Randomness of the Residuals5.4. Forecasting5.5. Order Selection5.5.1. The FPE Criterion5.5.2. The AICC CriterionProblems6. Nonstationary and Seasonal Time Series Models6.1. ARIMA Models for Nonstationary Time Series6.2. Identification Techniques6.3. Unit Roots in Time Series Models6.3.1. Unit Roots in Autoregressions6.3.2. Unit Roots in Moving Averages6.4. Forecasting ARIMA Models6.4.1. The Forecast Function6.5. Seasonal ARIMA Models6.5.1. Forecasting SARIMA Processes6.6. Regression with ARMA Errors6.6.1. OLS and GLS Estimation6.6.2. ML EstimationProblems7. Multivariate Time Series7.1. Examples7.2. Second-Order Properties of Multivariate Time Series7.3. Estimation of the Mean and Covariance Function7.3.1. Estimation of7.3.2. Estimation of F(h)7.3.3. Testing for Independence of Two Stationary Time Series7.3.4. Bartlett's Formula7.4. Multivariate ARMA Processes7.4.1. The Covariance Matrix Function of a Causal ARMAProcess7.5. Best Linear Predictors of Second-Order Random Vectors7.6. Modeling and Forecasting with Multivariate AR Processes7.6.1. Estimation for Autoregressive Processes Using Whittle'sAlgorithm7.6.2. Forecasting Multivariate Autoregressive Processes7.7. CointegrationProblems8. State-Space Models8.1. State-Space Representations8.2. The Basic Structural Model8.3. State-Space Representation of ARIMA Models8.4. The Kalman Recursions8.5. Estimation For State-Space Models8.6. State-Space Models with Missing Observations8.7. The EM Algorithm8.8. Generalized State-Space Models8.8.1. Parameter-Driven Models8.8.2. Observation-Driven ModelsProblems9. Forecasting Techniques9.1. The ARAR Algorithm9.1.1. Memory Shortening9.1.2. Fitting a Subset Autoregression9.1.3. Forecasting9.1.4. Application of the ARAR Algorithm9.2. The Holt-Winters Algorithm9.2.1. The Algorithm9.2.2. Holt-Winters and ARIMA Forecasting9.3. The Holt-Winters Seasonal Algorithm9.3.1. The Algorithm9.3.2. Holt-Winters Seasonal and ARIMA Forecasting9.4. Choosing a Forecasting AlgorithmProblems10. Further Topics10.1. Transfer Function Models10.1.1. Prediction Based on a Transfer Function Model10.2. Intervention Analysis10.3. Nonlinear Models10.3.1. Deviations from Linearity10.3.2. Chaotic Deterministic Sequences10.3.3. Distinguishing Between White Noise and iid Sequences10.3.4. Three Useful Classes of Nonlinear Models10.3.5. Modeling Volatility10.4. Continuous-Time Models10.5. Long-Memory ModelsProblemsA. Random Variables and Probability DistributionsA. 1. Distribution Functions and ExpectationA.2. Random VectorsA.3. The Multivariate Normal DistributionProblemsB Statistical ComplementsC Mean Square ConvergenceD An ITSM TutorialReferencesIndex

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《時(shí)間序列與預(yù)測(cè)(英文版第2版)》全面介紹了經(jīng)濟(jì)學(xué)、工程學(xué)、自然科學(xué)和社會(huì)科學(xué)中所用的時(shí)間序列和預(yù)測(cè)方法，核心內(nèi)容是平穩(wěn)過(guò)程、ARMA過(guò)程、ARIMA過(guò)程、多變量時(shí)間序列、狀態(tài)空間模型和譜分析。另外，《時(shí)間序列與預(yù)測(cè)(英文版第2版)》還介紹了Burg算法、Hannan—Riissanen算法、EM算法、結(jié)構(gòu)模型、指數(shù)平滑、轉(zhuǎn)移函數(shù)模型、非線性模型、連續(xù)時(shí)間模型和長(zhǎng)記憶模型等，每章的末尾都有大量習(xí)題，供讀者鞏固所學(xué)概念和方法，《時(shí)間序列與預(yù)測(cè)(英文版第2版)》強(qiáng)調(diào)方法和數(shù)據(jù)集的分析，配有時(shí)間序列軟件包ITSM2000的學(xué)生版?！稌r(shí)間序列與預(yù)測(cè)(英文版第2版)》適合作為各專業(yè)學(xué)生時(shí)間序列入門課程的教材，也適合其他有興趣的科研工作者閱讀。

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