Time series analysis / Wilfredo Palma, Ponticia Universidad Católica de Chile.
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 univar...
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Full Text (via ProQuest) |
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| Format: | eBook |
| Language: | English |
| Published: |
Hoboken, NJ :
John Wiley & Sons,
[2016]
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| Subjects: |
MARC
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| 100 | 1 | |a Palma, Wilfredo, |d 1963- |0 http://id.loc.gov/authorities/names/n2006088620 |1 http://isni.org/isni/0000000114806871 | |
| 245 | 1 | 0 | |a Time series analysis / |c Wilfredo Palma, Ponticia Universidad Católica de Chile. |
| 264 | 1 | |a Hoboken, NJ : |b John Wiley & Sons, |c [2016] | |
| 300 | |a 1 online resource | ||
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| 504 | |a Includes bibliographical references and indexes. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 520 | |a 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 CatOlica 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. | ||
| 505 | 0 | |a Title Page -- Copyright -- Table of Contents -- PREFACE -- ACKNOWLEDGMENTS -- ACRONYMS -- ABOUT THE COMPANION WEBSITE -- CHAPTER 1: INTRODUCTION -- 1.1 TIME SERIES DATA -- 1.2 RANDOM VARIABLES AND STATISTICAL MODELING -- 1.3 DISCRETE-TIME MODELS -- 1.4 SERIAL DEPENDENCE -- 1.5 NONSTATIONARITY -- 1.6 WHITENESS TESTING -- 1.7 PARAMETRIC AND NONPARAMETRIC MODELING -- 1.8 FORECASTING -- 1.9 TIME SERIES MODELING -- 1.10 BIBLIOGRAPHIC NOTES -- Problems -- CHAPTER 2: LINEAR PROCESSES -- 2.1 DEFINITION -- 2.2 STATIONARITY -- 2.3 INVERTIBILITY -- 2.4 CAUSALITY -- 2.5 REPRESENTATIONS OF LINEAR PROCESSES -- 2.6 WEAK AND STRONG DEPENDENCE -- 2.7 ARMA MODELS -- 2.8 AUTOCOVARIANCE FUNCTION -- 2.9 ACF AND PARTIAL ACF FUNCTIONS -- 2.10 ARFIMA PROCESSES -- 2.11 FRACTIONAL GAUSSIAN NOISE -- 2.12 BIBLIOGRAPHIC NOTES -- Problems -- CHAPTER 3: STATE SPACE MODELS -- 3.1 INTRODUCTION -- 3.2 LINEAR DYNAMICAL SYSTEMS -- 3.3 STATE SPACE MODELING OF LINEAR PROCESSES -- 3.4 STATE ESTIMATION -- 3.5 EXOGENOUS VARIABLES -- 3.6 BIBLIOGRAPHIC NOTES -- Problems -- CHAPTER 4: SPECTRAL ANALYSIS -- 4.1 TIME AND FREQUENCY DOMAINS -- 4.2 LINEAR FILTERS -- 4.3 SPECTRAL DENSITY -- 4.4 PERIODOGRAM -- 4.5 SMOOTHED PERIODOGRAM -- 4.6 EXAMPLES -- 4.7 WAVELETS -- 4.8 SPECTRAL REPRESENTATION -- 4.9 TIME-VARYING SPECTRUM -- 4.10 BIBLIOGRAPHIC NOTES -- Problems -- CHAPTER 5: ESTIMATION METHODS -- 5.1 MODEL BUILDING -- 5.2 PARSIMONY -- 5.3 AKAIKE AND SCHWARTZ INFORMATION CRITERIA -- 5.4 ESTIMATION OF THE MEAN -- 5.5 ESTIMATION OF AUTOCOVARIANCES -- 5.6 MOMENT ESTIMATION -- 5.7 MAXIMUM-LIKELIHOOD ESTIMATION -- 5.8 WHITTLE ESTIMATION -- 5.9 STATE SPACE ESTIMATION -- 5.10 ESTIMATION OF LONG-MEMORY PROCESSES -- 5.11 NUMERICAL EXPERIMENTS -- 5.12 BAYESIAN ESTIMATION -- 5.13 STATISTICAL INFERENCE -- 5.14 ILLUSTRATIONS -- 5.15 BIBLIOGRAPHIC NOTES -- Problems -- CHAPTER 6: NONLINEAR TIME SERIES. | |
| 505 | 8 | |a 6.1 INTRODUCTION -- 6.2 TESTING FOR LINEARITY -- 6.3 HETEROSKEDASTIC DATA -- 6.4 ARCH MODELS -- 6.5 GARCH MODELS -- 6.6 ARFIMA-GARCH MODELS -- 6.7 ARCH(∞) MODELS -- 6.8 APARCH MODELS -- 6.9 STOCHASTIC VOLATILITY -- 6.10 NUMERICAL EXPERIMENTS -- 6.11 DATA APPLICATIONS -- 6.12 VALUE AT RISK -- 6.13 AUTOCORRELATION OF SQUARES -- 6.14 THRESHOLD AUTOREGRESSIVE MODELS -- 6.15 BIBLIOGRAPHIC NOTES -- Problems -- CHAPTER 7: PREDICTION -- 7.1 OPTIMAL PREDICTION -- 7.2 ONE-STEP AHEAD PREDICTORS -- 7.3 MULTISTEP AHEAD PREDICTORS -- 7.4 HETEROSKEDASTIC MODELS -- 7.5 PREDICTION BANDS -- 7.6 DATA APPLICATION -- 7.7 BIBLIOGRAPHIC NOTES -- Problems -- CHAPTER 8: NONSTATIONARY PROCESSES -- 8.1 INTRODUCTION -- 8.2 UNIT ROOT TESTING -- 8.3 ARIMA PROCESSES -- 8.4 LOCALLY STATIONARY PROCESSES -- 8.5 STRUCTURAL BREAKS -- 8.6 BIBLIOGRAPHIC NOTES -- Problems -- CHAPTER 9: SEASONALITY -- 9.1 SARIMA MODELS -- 9.2 SARFIMA MODELS -- 9.3 GARMA MODELS -- 9.4 CALCULATION OF THE ASYMPTOTIC VARIANCE -- 9.5 AUTOCOVARIANCE FUNCTION -- 9.6 MONTE CARLO STUDIES -- 9.7 ILLUSTRATION -- 9.8 BIBLIOGRAPHIC NOTES -- Problems -- CHAPTER 10: TIME SERIES REGRESSION -- 10.1 MOTIVATION -- 10.2 DEFINITIONS -- 10.3 PROPERTIES OF THE LSE -- 10.4 PROPERTIES OF THE BLUE -- 10.5 ESTIMATION OF THE MEAN -- 10.6 POLYNOMIAL TREND -- 10.7 HARMONIC REGRESSION -- 10.8 ILLUSTRATION: AIR POLLUTION DATA -- 10.9 BIBLIOGRAPHIC NOTES -- Problems -- CHAPTER 11: MISSING VALUES AND OUTLIERS -- 11.1 INTRODUCTION -- 11.2 LIKELIHOOD FUNCTION WITH MISSING VALUES -- 11.3 EFFECTS OF MISSING VALUES ON ML ESTIMATES -- 11.4 EFFECTS OF MISSING VALUES ON PREDICTION -- 11.5 INTERPOLATION OF MISSING DATA -- 11.6 SPECTRAL ESTIMATION WITH MISSING VALUES -- 11.7 OUTLIERS AND INTERVENTION ANALYSIS -- 11.8 BIBLIOGRAPHIC NOTES -- Problems -- CHAPTER 12: NON-GAUSSIAN TIME SERIES -- 12.1 DATA DRIVEN MODELS -- 12.2 PARAMETER DRIVEN MODELS. | |
| 505 | 8 | |a 12.3 ESTIMATION -- 12.4 DATA ILLUSTRATIONS -- 12.5 ZERO-INFLATED MODELS -- 12.6 BIBLIOGRAPHIC NOTES -- Problems -- APPENDIX A: COMPLEMENTS -- A.1 PROJECTION THEOREM -- A.2 WOLD DECOMPOSITION -- A.3 BIBLIOGRAPHIC NOTES -- APPENDIX B: SOLUTIONS TO SELECTED PROBLEMS -- APPENDIX C: DATA AND CODES -- REFERENCES -- TOPIC INDEX -- AUTHOR INDEX -- End User License Agreement. | |
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