Time Series Analysis
eBook - PDF

Time Series Analysis

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Time Series Analysis

About this book

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.

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Yes, you can access Time Series Analysis by James D. Hamilton,James Douglas Hamilton in PDF and/or ePUB format, as well as other popular books in Business & Investments & Securities. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Half-title Page
  3. Title Page
  4. Copyright Page
  5. Contents
  6. Preface
  7. 1 Difference Equations
  8. 2. Lag Operators
  9. 3. Stationary ARMA Processes
  10. 4. Forecasting
  11. 5. Maximum Likelihood Estimation
  12. 6. Spectral Analysis
  13. 7. Asymptotic Distribution Theory
  14. 8. Linear Regression Models
  15. 9. Linear Systems of Simultaneous Equations
  16. 10. Covariance-Stationary Vector Processes
  17. 11. Vector Autoregressions
  18. 12. Bayesian Analysis
  19. 13. The Kalman Filter
  20. 14. Generalized Method of Moments
  21. 15. Models of Nonstationary Time Series
  22. 16. Processes with Deterministic Time Trends
  23. 17. Univariate Processes with Unit Roots
  24. 18. Unit Roots in Multivariate Time Series
  25. 19. Cointegration
  26. 20. Full-Information Maximum Likelihood Analysis of Cointegrated Systems
  27. 21. Time Series Models of Heteroskedasticity
  28. 22. Modeling Time Series with Changes in Regime
  29. A. Mathematical Review
  30. B. Statistical Tables
  31. C. Answers to Selected Exercises
  32. D. Greek Letters and Mathematical Symbols Used in the Text
  33. Author Index
  34. Subject Index