Time Series Analysis

Regular price €82.99
A01=James D. Hamilton
Accuracy and precision
Asymptotic distribution
Author_James D. Hamilton
Autocorrelation
Autocovariance
Autoregressive conditional heteroskedasticity
Autoregressive model
Autoregressive-moving-average model
Bayesian inference
Box-Jenkins
Canonical correlation
Category=KCH
Category=PBT
Central limit theorem
Chi-squared test
Coefficient
Cointegration
Confidence region
Constant term
Covariance matrix
Dickey-Fuller test
Dummy variable (statistics)
Eigenvalues and eigenvectors
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Equation
Error term
Errors and residuals
Estimation
Estimator
Existential quantification
Expectation-maximization algorithm
F-distribution
F-test
Fisher information
Forecast error
Forecasting
Generalized method of moments
Granger causality
Inference
Kalman filter
Least squares
Likelihood function
Likelihood-ratio test
Linear regression
Markov chain
Mathematical optimization
Maximum a posteriori estimation
Maximum likelihood estimation
Newton's method
Nuisance parameter
Null hypothesis
Order condition
Parameter
Prior probability
Probability
Projection (linear algebra)
Quasi-maximum likelihood estimate
Random variable
Rate of convergence
Simultaneous equations
Special case
Standard deviation
Standard error
Stationary process
Statistic
Statistical hypothesis testing
Student's t-test
T-statistic
Time series
Unit root
Unit root test
Variable (mathematics)
Variance
Vector autoregression

Product details

  • ISBN 9780691042893
  • Weight: 1701g
  • Dimensions: 152 x 229mm
  • Publication Date: 31 Jan 1994
  • Publisher: Princeton University Press
  • Publication City/Country: US
  • Product Form: Hardback
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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.
James D. Hamilton is professor of economics at the University of California, San Diego.