Econometric Theory and Methods

Name: Econometric Theory and Methods
Price: 40.97 USD
Availability: InStock
ISBN: 9780195123722

ISBN-10: 0195123727

ISBN-13: 9780195123722

Edition: 2004

Authors: Russell Davidson, James G. MacKinnon

List price: $209.99

This item qualifies for FREE shipping.

30 day, 100% satisfaction guarantee!

Sell

Get cash fast!

Buy new: $196.14

Marketplace

3 new & used from $40.97

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

Description:

This text provides a unified treatment of modern econometric theory and practical econometric methods. The geometrical approach to least squares is emphasized, as is the method of moments, which is used to motivate a wide variety of estimators and tests. Simulation methods, including the bootstrap, are introduced early and used extensively. The book deals with a large number of modern topics. In addition to bootstrap and Monte Carlo tests, these include sandwich covariance matrix estimators, artificial regressions, estimating functions and the generalized method of moments, indirect inference, and kernel estimation. Every chapter incorporates numerous exercises, some theoretical, some…

Book details

List price: $209.99
Copyright year: 2004
Publisher: Oxford University Press, Incorporated
Publication date: 10/16/2003
Binding: Hardcover
Pages: 768
Size: 9.41" wide x 6.30" long x 1.50" tall
Weight: 2.860



Preface


Data, Solutions, and Corrections



Regression Models



Introduction



Distributions, Densities, and Moments



The Specification of Regression Models



Matrix Algebra



Method-of-Moments Estimation



Notes on Exercises



Exercises



The Geometry of Linear Regression



Introduction



The Geometry of Vector Spaces



The Geometry of OLS Estimation



The Frisch-Waugh-Lowell Theorem



Applications of the FWL Theorem



Influential Observations and Leverage



Final Remarks



Exercises



The Statistical Properties of Ordinary Least Squares



Introduction



Are OLS Parameter Estimators Unbiased?



Are OLS Parameter Estimators Consistent?



The Covariance Matrix of the OLS Parameter Estimates



Efficiency of the OLS Estimator



Residuals and Error Terms



Misspecification of Linear Regression Models



Measures of Goodness of Fit



Final Remarks



Exercises



Hypothesis Testing in Linear Regression Models



Introduction



Basic Ideas



Some Common Distractions



Exact Tests in the Classical Normal Linear Model



Large-Sample Tests in Linear Regression Models



Simulation-Based Tests



The Power of Hypothesis Tests



Final Remarks



Exercises



Confidence Intervals



Introduction



Exact and Asymptotic Confidence Intervals



Bootstrap Confidence Intervals



Confidence Regions



Heteroskedasticity-Consistent Covariance Matrices



The Delta Method



Final Remarks



Exercises



Nonlinear Regression



Introduction



Method-of-Moments Estimators for Nonlinear Models



Nonlinear Least Squares



Computing NLS Estimates



The Gauss-Newton Regression



One-Step Estimation



Hypothesis Testing



Heteroskedasticity-Robust Tests



Final Remarks



Exercises



Generalized Least Squares and Related Topics



Introduction



The GLS Eliminator



Computing GLS Estimates



Feasible Generalized Least Squares



Heteroskedasticity



Autoregressive and Moving-Average Processes



Testing for Serial Correlation



Estimating Models with Autoregressive Errors



Specification Testing and Serial Correlation



Models for Panel Data



Final Remarks



Exercises



Instrumental Variables Estimation



Introduction



Correlation Between Error Terms and Regressors



Instrumental Variables Estimation



Finite-Sample Properties of IV Estimators



Hypothesis Testing



Testing Overidentifying Restrictions



Durbin-Wu-Hausman Tests



Bootstrap Tests



IV Estimation of Nonlinear Models



Final Remarks



Exercises



The Generalized Methods of Moments



Introduction



GMM Estimators for Linear Regression Models



HAC Covariance Matrix Estimation



Tests Based on the GMM Criterion Function



GMM Estimators for Nonlinear Models



The Method of Simulated Moments



Final Remarks



Exercises



The Method of Maximum Likelihood



Introduction



Basic Concepts of Maximum Likelihood Estimation



Asymptotic Propertied of ML Estimators



The Covariance Matrix of the ML Estimator



Hypothesis Testing



The Asymptotic Theory of the Three Classical Tests



ML Estimation of Models with Autoregressive Errors



Transformations of the Dependent Variable



Final Remarks



Exercises



Discrete and Limited Dependent Variables



Introduction1



Binary Response Models: Estimation1



Binary Response Models: Inference



Models for More than Two Discrete Responses



Models for Count Data



Models for Censored and Truncated Data



Sample Selectivity



Duration Models



Final Remarks



Exercises



Multivariate Models



Introduction



Seemingly Unrelated Linear Regressions



Systems of Nonlinear Regressions



Linear Simultaneous Equations Models



Maximum Likelihood Estimation



Nonlinear Simultaneous Equations Models



Final Remarks



Appendix: Detailed Results on FIML and LIML



Exercises



Methods for Stationary Time-Series Data


Introduction