.

0 (hodnocen0 x )

(1) Půjčeno:1x 

BK

2nd ed.

New York, Springer, 2008

xiii, 491 s. : il., grafy ; 24 cm

ISBN 978-0-387-75958-6 (váz.)

Springer texts in statistics

Obsahuje tabulky

Bibliografie na s. 477-486, rejstřík

000044212

Springer Texts in Statistics // Tune Series Analysis: With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticty, and threshold models. All of the ideas and methods are illustrated with both real and simulated data sets. // A unique feature of this edition is its integration with the R computing environment. The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text. Script files of R commands for each chapter are available for download. There is also an extensive appendix in the book that leads the reader through the use of R commands and the new R package to carry out the analyses. // Jonathan D. Cryer is Professor Emeritus, University of Iowa, in the Department of Statistics and Actuarial Science. He is a Fellow of the American Statistical Association and received a Collegiate Teaching Award from the University of Iowa College of Liberal Arts and Sciences. He is the author of Statistics for Business: Data Analysis and Modeling, Second Edition, (with Robert B. Miller), the Minitab Handbook, Fifth Edition, (with Barbara Ryan and Brian

Joiner), the Electronic Companion to Statistics (with George Cobb), Electronic Companion to Business Statistics (with George Cobb) and numerous research papers. // Kung-Sik Chan is Professor, University of Iowa, in the Department of Statistics and Actuarial Science. He is a Fellow of the American Statistical Association and the Institute of the Mathematical Statistics, and an Elected Member of the International Statistical Institute. He received a Faculty Scholar Award from the University of Iowa in 1996. He is the author of Chaos: A Statistical Perspective (with Howell Tong) and numerous research papers. // STATISTICS // ISBN // -0-387-75958-6 // 9 // > springer.com // Contents // Chapter 1 Introduction...i // 1.1 Examples of Time Series...1 // 1.2 A Model-Building Strategy...8 // 1.3 Time Series Plots in History...8 // 1.4 An Overview of the Book...9 // Exercises...10 // Chapter 2 Fundamental Concepts...11 // 2.1 Time Series and Stochastic Processes...11 // 2.2 Means, Variances, and Covariances...11 // 2.3 Stationarity...16 // 2.4 Summary...19 // Exercises...19 // Appendix A: Expectation, Variance, Covariance, and Correlation. 24 // Chapter 3 Trends...21 // 3.1 Deterministic Versus Stochastic Trends...27 // 3.2 Estimation of a Constant Mean...28 // 3.3 Regression Methods...30 // 3.4 Reliability and Efficiency of Regression Estimates...36 // 3.5 Interpreting Regression Output...40 // 3.6 Residual Analysis...42 // 3.7 Summary...50 // Exercises...50 // Chapter 4 Models for Stationary Time Series..55

// 4.1 General Linear Processes...55 // 4.2 Moving Average Processes...57 // 4.3 Autoregressive Processes...66 // 4.4 The Mixed Autoregressive Moving Average Model...77 // 4.5 Invertibility...79 // 4.6 Summary...80 // Exercises...81 // Appendix B: The Stationarity Region for an AR(2) Process.84 // Appendix C: The Autocorrelation Function for ARMA(p,g)...85 // x Contents // Chapter 5 Models for Nonstationary Time Series .87 // 5.1 Stationarity Through Differencing...88 // 5.2 ARIMA Models...92 // 5.3 Constant Terms in ARIMA Models...97 // 5.4 Other Transformations...98 // 5.5 Summary...102 // Exercises...103 // Appendix D: The Backshift Operator...106 // Chapter 6 Model Specification...109 // 6.1 Properties of the Sample Autocorrelation Function...109 // 6.2 The Partial and Extended Autocorrelation Functions...112 // 6.3 Specification of Some Simulated Time Series...117 // 6.4 Nonstationarity...125 // 6.5 Other Specification Methods...130 // 6.6 Specification of Some Actual Time Series...133 // 6.7 Summary...141 // Exercises...141 // Chapter 7 Parameter Estimation...149 // 7.1 The Method of Moments...149 // 7.2 Least Squares Estimation...154 // 7.3 Maximum Likelihood and Unconditional Least Squares ... 158 // 7.4 Properties of the Estimates...160 // 7.5 Illustrations of Parameter Estimation...163 // 7.6 Bootstrapping ARIMA Models...167 // 7.7 Summary...170 // Exercises...170 // Chapter 8 Model Diagnostics...175 // 8.1 Residual Analysis...175 // 8.2 Overfitting and Parameter Redundancy...185

// 8.3 Summary...188 // Exercises...188 // Contents // XI // Chapter 9 Forecasting...191 // 9.1 Minimum Mean Square Error Forecasting...191 // 9.2 Deterministic Trends...191 // 9.3 ARIMA Forecasting...193 // 9.4 Prediction Limits...203 // 9.5 Forecasting Illustrations...204 // 9.6 Updating ARIMA Forecasts...207 // 9.7 Forecast Weights and Exponentially Weighted // Moving Averages...207 // 9.8 Forecasting Transformed Series...209 // 9.9 Summary of Forecasting with Certain ARIMA Models... 211 // 9.10 Summary...213 // Exercises...213 // Appendix E: Conditional Expectation...218 // Appendix F: Minimum Mean Square Error Prediction...218 // Appendix G: The Truncated Linear Process...221 // Appendix H: State Space Models...222 // Chapter 10 Seasonal Models...227 // 10.1 Seasonal ARIMA Models...228 // 10.2 Multiplicative Seasonal ARMA Models...230 // 10.3 Nonstationary Seasonal ARIMA Models...233 // 10.4 Model Specification, Fitting, and Checking...234 // 10.5 Forecasting Seasonal Models...241 // 10.6 Summary...246 // Exercises...246 // Chapter 11 Time Series Regression Models...249 // 11.1 Intervention Analysis...249 // 11.2 Outliers...257 // 11.3 Spurious Correlation...260 // 11.4 Prewhitening and Stochastic Regression...265 // 11.5 Summary...273 // Exercises...274 // XII // Contents // Chapter 12 Time Series Models of // HETEROSCEDASTICITY...277 // 12.1 Some Common Features of Financial Time Series...278 // 12.2 The ARCH(1) Model...285 // 12.3 GARCH Models...289 // 12.4 Maximum Likelihood

Estimation...298 // 12.5 Model Diagnostics...301 // 12.6 Conditions for the Nonnegativity of the // Conditional Variances...307 // 12.7 Some Extensions of the GARCH Model...310 // 12.8 Another Example: The Daily USD/HKD Exchange Rates ..311 // 12.9 Summary...315 // Exercises...316 // Appendix I: Formulas for the Generalized Portmanteau Tests .. .318 // Chapter 13 Introduction to Spectral Analysis. .. .319 // 13.1 Introduction...319 // 13.2 The Periodogram...322 // 13.3 The Spectral Representation and Spectral Distribution... .327 // 13.4 The Spectral Density...330 // 13.5 Spectral Densities for ARMA Processes...332 // 13.6 Sampling Properties of the Sample Spectral Density..340 // 13.7 Summary...346 // Exercises...346 // Appendix J: Orthogonality of Cosine and Sine Sequences...349 // Chapter 14 Estimating the Spectrum...351 // 14.1 Smoothing the Spectral Density...351 // 14.2 Bias and Variance...354 // 14.3 Bandwidth...355 // 14.4 Confidence Intervals for the Spectrum...356 // 14.5 Leakage and Tapering...358 // 14.6 Autoregressive Spectrum Estimation...363 // 14.7 Examples with Simulated Data...364 // 14.8 Examples with Actual Data...370 // 14.9 Other Methods of Spectral Estimation...376 // 14.10 Summary...378 // Exercises...378 // Appendix K: Tapering and the Dirichlet Kernel...381 // Contents xiii // Chapter 15 Threshold Models...??? // 15.1 Graphically Exploring Nonlinearity...384 // 15.2 Tests for Nonlinearity...390 // 15.3 Polynomial Models Are Generally Explosive...393

15.4 First-Order Threshold Autoregressive Models...395 // 15.5 Threshold Models...399 // 15.6 Testing for Threshold Nonlinearity...400 // 15.7 Estimation of a TAR Model...402 // 15.8 Model Diagnostics...411 // 15.9 Prediction...415 // 15.10 Summary...420 // Exercises...420 // Appendix L: The Generalized Portmanteau Test for TAR.421 // Chapter 16 Appendix: An Introduction to R...423 // Introduction...423 // Chapter 1 R Commands...429 // Chapter 2 R Commands...433 // Chapter 3 R Commands...433 // Chapter 4 R Commands...438 // Chapter 5 R Commands...439 // Chapter 6 R Commands...441 // Chapter 7 R Commands...442 // Chapter 8 R Commands...446 // Chapter 9 R Commands...447 // Chapter 10 R Commands...450 // Chapter 11 R Commands...451 // Chapter 12 R Commands...457 // Chapter 13 R Commands...460 // Chapter 14 R Commands...461 // Chapter 15 R Commands...462 // New or Enhanced Functions in the TSA Library...468 // Dataset Information...471 // Bibliography...477 // Index // 487