Time Series Analysis

Time Series Analysis

andreyma Books Reviews

Review Time Series Analysis 

by JAMES D. HAMILTON

Description

It can be noted that large shifts and reconstructions took place in the economic and financial systems. These changes led to the formulation of new approaches in order for researchers to fully analyze economic and financial time series. To present recent developments in the landscape and to intellectually curate into a comprehensive compendium, Time Series Analysis was written. With great simplicity, this became a primer published specially for first-year graduate students who take an interest in the field. 

Integrated within the book are the latest trends and developments, including autoregressions, generalized method of moments, the economic and statistical ramifications of unit roots, time-dependent variances, and nonlinear time series frameworks. Together with the aforementioned, the author infused discussions on basic tools ranging from linear representations up to Kalman filter. The book’s overall structure allows an efficient application of theories as it is complete with economic theory, econometrics, and updated results ready for practice. 

About the Author

JAMES DOUGLAS HAMILTON is a renowned figure in the field of econometrics. He currently teaches at the University of California, San Diego, and some of his most influential and highly commendable works are dedicated to the field of time series and energy economics. 

Table of Contents 

Preface

  • Chapter 1- Difference Equations
  • Chapter 2- Lag Operators
  • Chapter 3- Stationary ARMA Process
  • Chapter 4- Forecasting
  • Chapter 5- Maximum Likelihood Estimation
  • Chapter 6- Spectral Analysis
  • Chapter 7- Asymptotic Distribution Theory
  • Chapter 8- Linear Regression Models
  • Chapter 9- Linear Systems of Simultaneous Equations
  • Chapter 10- Covariance-Stationary Vector Processes
  • Chapter 11- Vector Autoregressions 
  • Chapter 12- Bayesian Analysis
  • Chapter 13- The Kalman Filter
  • Chapter 15- Generalized Method of Moments
  • Chapter 16- Processes with Deterministic Time Trends
  • Chapter 17- Univariate Processes with Unit Roots 
  • Chapter 18- Unit Roots in Multivariate Time Series
  • Chapter 19- Cointegration
  • Chapter 20- Full-Information Maximum Likelihood Analysis of Cointegrated Systems
  • Chapter 21- Time Series Models of Heteroskedasticity
  • Chapter 22- Modeling Time Series with Changes in Regime
  1. Mathematical Review
  2. Statistical Tables
  3. Answers to Selected Exercises
  4. Greek Letters and Mathematical Symbols

Author Index

Subject index