This broadly based graduate-level textbook covers the major models and statistical tools currently used in the practice of econometrics. It examines the classical, the decision theory, and the Bayesian approaches, and contains material on single equation and simultaneous equation econometric models. Includes an extensive reference list for each topic.
This broadly based graduate-level textbook covers the major models and statistical tools currently used in the practice of econometrics. It examines the classical, the decision theory, and the Bayesian approaches, and contains material on single equation and simultaneous equation econometric models. Includes an extensive reference list for each topic.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
George Judge is an American econometrician and Professor in the Graduate School in the Department of Agricultural and Resource Economics located in the UC Berkeley College of Natural Resources. William E. Griffiths is the author of The Theory and Practice of Econometrics, 2nd Edition, published by Wiley.
Inhaltsangabe
SAMPLING THEORY AND BAYESIAN APPROACHES TO INFERENCE. The Classical Inference Approach for the General LinearModel. Statistical Decision Theory and Biased Estimation. The Bayesian Approach to Inference. INFERENCE IN GENERAL STATISTICAL MODELS AND TIME SERIES. Some Asymptotic Theory and Other General Results for the LinearStatistical Model. Nonlinear Statistical Models. Time Series. DYNAMIC SPECIFICATIONS. Autocorrelation. Finite Distributed Lags. Infinite Distributed Lags. SOME ALTERNATIVE COVARIANCE STRUCTURES. Heteroskedasticity. Disturbance-Related Sets of Regression Equations. Inference in Models that Combine Time Series and Cross-SectionalData. INFERENCE IN SIMULTANEOUS EQUATION MODELS. Specification and Identification in Simultaneous EquationModels. Estimation and Inference in a System of SimultaneousEquations. Multiple Time Series and Systems of Dynamic SimultaneousEquations. FURTHER MODEL EXTENSIONS. Unobservable Variables. Qualitative and Limited Dependent Variable Models. Varying and Random Coefficient Models. Non-Normal Disturbances. On Selecting the Set of Aggressors. Multicollinearity. Appendices.
SAMPLING THEORY AND BAYESIAN APPROACHES TO INFERENCE. The Classical Inference Approach for the General LinearModel. Statistical Decision Theory and Biased Estimation. The Bayesian Approach to Inference. INFERENCE IN GENERAL STATISTICAL MODELS AND TIME SERIES. Some Asymptotic Theory and Other General Results for the LinearStatistical Model. Nonlinear Statistical Models. Time Series. DYNAMIC SPECIFICATIONS. Autocorrelation. Finite Distributed Lags. Infinite Distributed Lags. SOME ALTERNATIVE COVARIANCE STRUCTURES. Heteroskedasticity. Disturbance-Related Sets of Regression Equations. Inference in Models that Combine Time Series and Cross-SectionalData. INFERENCE IN SIMULTANEOUS EQUATION MODELS. Specification and Identification in Simultaneous EquationModels. Estimation and Inference in a System of SimultaneousEquations. Multiple Time Series and Systems of Dynamic SimultaneousEquations. FURTHER MODEL EXTENSIONS. Unobservable Variables. Qualitative and Limited Dependent Variable Models. Varying and Random Coefficient Models. Non-Normal Disturbances. On Selecting the Set of Aggressors. Multicollinearity. Appendices.
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