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Time Series Analysis by James D. Hamilton
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    Description of Time Series Analysis

    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.

    Contents of Time Series Analysis

    Preface

    1 Difference Equations
    2 Lag Operators
    3 Stationary ARMA Processes
    4 Forecasting
    5 Maximum Likelihood Estimation
    6 Spectral Analysis
    7 Asymptotic Distribution Theory
    8 Linear Regression Models
    9 Linear Systems of Simultaneous Equations
    10 CovarianceStationary Vector Processes
    11 Vector Autoregressions
    12 Bayesian Analysis
    13 The Kalman Filter
    14 Generalized Method of Moments
    15 Models of Nonstationary Time Series
    16 Processes with Deterministic Time Trends
    17 Univariate Processes with Unit Roots
    18 Unit Roots in Multivariate Time Series
    19 Cointegration
    20 FullInformation Maximum Likelihood Analysis of Cointegrated Systems
    21 Time Series Models of Heteroskedasticity
    22 Modeling Time Series with Changes in Regime

    A Mathematical Review
    B Statistical Tables
    C Answers to Selected Exercises
    D Greek Letters and Mathematical Symbols Used in the Text
    Author Index


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