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eBay-Artikelnr.:127182994832
Artikelmerkmale
- Artikelzustand
- ISBN
- 9780691042893
Über dieses Produkt
Product Identifiers
Publisher
Princeton University Press
ISBN-10
0691042896
ISBN-13
9780691042893
eBay Product ID (ePID)
314676
Product Key Features
Number of Pages
816 Pages
Language
English
Publication Name
Time Series Analysis
Subject
Probability & Statistics / Time Series, Investments & Securities / General
Publication Year
1994
Type
Textbook
Subject Area
Mathematics, Business & Economics
Format
Hardcover
Dimensions
Item Height
2.1 in
Item Weight
60 Oz
Item Length
9.8 in
Item Width
7 in
Additional Product Features
Intended Audience
College Audience
LCCN
93-004958
Reviews
A carefully prepared and well written book. . . . Without doubt, it can be recommended as a very valuable encyclopedia and textbook for a reader who is looking for a mainly theoretical textbook which combines traditional time series analysis with a review of recent research areas., A carefully prepared and well written book. . . . Without doubt, it can be recommended as a very valuable encyclopedia and textbook for a reader who is looking for a mainly theoretical textbook which combines traditional time series analysis with a review of recent research areas. -- Journal of Economics, "A carefully prepared and well written book. . . . Without doubt, it can be recommended as a very valuable encyclopedia and textbook for a reader who is looking for a mainly theoretical textbook which combines traditional time series analysis with a review of recent research areas."-- Journal of Economics, "A carefully prepared and well written book. . . . Without doubt, it can be recommended as a very valuable encyclopedia and textbook for a reader who is looking for a mainly theoretical textbook which combines traditional time series analysis with a review of recent research areas." -- Journal of Economics
Dewey Edition
20
Illustrated
Yes
Dewey Decimal
519.55
Table Of Content
Preface 1 Difference Equations 1 2 Lag Operators 25 3 Stationary ARMA Processes 43 4 Forecasting 72 5 Maximum Likelihood Estimation 117 6 Spectral Analysis 152 7 Asymptotic Distribution Theory 180 8 Linear Regression Models 200 9 Linear Systems of Simultaneous Equations 233 10 Covariance-Stationary Vector Processes 257 11 Vector Autoregressions 291 12 Bayesian Analysis 351 13 The Kalman Filter 372 14 Generalized Method of Moments 409 15 Models of Nonstationary Time Series 435 16 Processes with Deterministic Time Trends 454 17 Univariate Processes with Unit Roots 475 18 Unit Roots in Multivariate Time Series 544 19 Cointegration 571 20 Full-Information Maximum Likelihood Analysis of Cointegrated Systems 630 21 Time Series Models of Heteroskedasticity 657 22 Modeling Time Series with Changes in Regime 677 A Mathematical Review 704 B Statistical Tables 751 C Answers to Selected Exercises 769 D Greek Letters and Mathematical Symbols Used in the Text 786 Author Index 789 Subject Index 792
Synopsis
The last decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. "Time Series Analysis" fills an important need for a textbook that integrates economic theory, econometrics, and new results. The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers., An authoritative, self-contained overview of time series analysis for students and researchers The past decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This textbook synthesizes these advances and makes them accessible to first-year graduate students. James Hamilton provides comprehensive treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems--including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter--in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results. This invaluable book starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers., A graduate-level text which describes the recent dramatic changes that have taken place in the way that researchers analyze economic and financial time series. It explores such important innovations as vector regression, nonlinear time series models and the generalized methods of moments., The last decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results. The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.
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