This Second Edition of the highly acclaimed introduction to econometrics retains its comprehensive nature and strong authorship, while incorporating much new material. New to this edition are a complete treatment of Bayesian inference, sampling theory, an appendix on linear algebra, and a computer handbook. Presentation covers modern statistical models and focuses on the sampling theory process by which the data were generated, and the statistical consequences of alternative decisions under uncertainty. Asymptotics are introduced early on, for use throughout. Includes at least one applied…mehr
This Second Edition of the highly acclaimed introduction to econometrics retains its comprehensive nature and strong authorship, while incorporating much new material. New to this edition are a complete treatment of Bayesian inference, sampling theory, an appendix on linear algebra, and a computer handbook. Presentation covers modern statistical models and focuses on the sampling theory process by which the data were generated, and the statistical consequences of alternative decisions under uncertainty. Asymptotics are introduced early on, for use throughout. Includes at least one applied example to illustrate each model, and contains many analytical and numerical exercises.
From the contents: THE FOUNDATION OF STATISTICAL INFERENCE. Probability and Distribution Theory. Statistical Inference: Estimation and Hypothesis Testing. Bayesian Inference THE GENERAL LINEAR STATISTICAL MODEL. Linear Statistical Model. The Normal General Linear Statistical Model. Bayesian Analysis of the Normal Linear Statistical Model GENERALIZATIONS OF THE LINEAR STATISTICAL MODEL. General Linear Statistical Model with Non-Scalar Identity Covariance Matrix. General Linear Statistical Model with an Unknown Covariance Matrix. Dummy Variables and Varying Parameter Models. Sets of Linear Statistical Models. Nonlinear Least Squares and Nonlinear Maximum Likelihood Estimation. Stochastic Regressors SIMULTANEOUS EQUATION MODELS. An Introduction to Simultaneous Linear Statistical Models. Estimation and Inference for Simultaneous Equation Statistical Models TIME-SERIES AND DISTRIBUTED LAG MODELS. Time Series Analysis and Forecasting. Distributed Lags. Multiple Time-Series ADDITIONAL ECONOMETRIC TOPICS. Qualitative and Limited Dependent Variable Models. Prior Information, Biased Estimation, and Statistical Model Selection. Multicollinearity. Robust Estimation. Epilogue. Appendix
From the contents: THE FOUNDATION OF STATISTICAL INFERENCE. Probability and Distribution Theory. Statistical Inference: Estimation and Hypothesis Testing. Bayesian Inference THE GENERAL LINEAR STATISTICAL MODEL. Linear Statistical Model. The Normal General Linear Statistical Model. Bayesian Analysis of the Normal Linear Statistical Model GENERALIZATIONS OF THE LINEAR STATISTICAL MODEL. General Linear Statistical Model with Non-Scalar Identity Covariance Matrix. General Linear Statistical Model with an Unknown Covariance Matrix. Dummy Variables and Varying Parameter Models. Sets of Linear Statistical Models. Nonlinear Least Squares and Nonlinear Maximum Likelihood Estimation. Stochastic Regressors SIMULTANEOUS EQUATION MODELS. An Introduction to Simultaneous Linear Statistical Models. Estimation and Inference for Simultaneous Equation Statistical Models TIME-SERIES AND DISTRIBUTED LAG MODELS. Time Series Analysis and Forecasting. Distributed Lags. Multiple Time-Series ADDITIONAL ECONOMETRIC TOPICS. Qualitative and Limited Dependent Variable Models. Prior Information, Biased Estimation, and Statistical Model Selection. Multicollinearity. Robust Estimation. Epilogue. Appendix
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