Econometrics A Modern Introduction

Name: Econometrics A Modern Introduction
Price: 28.6 USD
Availability: InStock
ISBN: 9780321113610

ISBN-10: 0321113616

ISBN-13: 9780321113610

Edition: 2006

Authors: Michael P. Murray

List price: $126.65

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"Econometrics: A Modern Introduction" conditions students to "think like econometricians" right from the start by opening with a unique Monte Carlo exercise, and connects econometrics to economic theory through a series of exemplary econometric analyses presented throughout the text. Students learn to critically evaluate economic conclusions through the use of original data and compelling topics such as discrimination, demand for cocaine, capital punishment, and infant mortality.

Book details

List price: $126.65
Copyright year: 2006
Publisher: Addison Wesley
Publication date: 11/14/2019
Binding: Paperback
Pages: 976
Size: 7.75" wide x 9.25" long x 1.50" tall
Weight: 3.630



Overview







What is Econometrics?



Choosing Estimators: Intuition and Monte Carlo Methods



Linear Estimators and the Gauss-Markov Theorem



BlueEstimators for the Slope and Intercept of a Straight Line



Residuals



Multiple Regression







Testing Single Hypotheses in Regression Models



Superfluous and Omitted Variables, Multicollinearityand Binary Variables



Testing Multiple Hypotheses







Heteroskedastic Disturbances



AutoregressiveDisturbances



Large Sample Properties Of Estimators: Consistencyand Asymptotic Efficiency



Instrumental Variables Estimation



Systems of Equations



Randomized Experimentsand Natural Experiments



Analyzing Panel Data



Forecasting



Stochastically Trending Variables



Logit and Probit Models: Truncated and Censored Samples Statistical


Appendix



Using Calculus And Algebra For The Simplest Case: N = 3



LOCAL AVERAGE TREATMENT EFFECTS



Generalized Method Of Moments Estimators And Identification



Maximum Likelihood Estimation



Estimators For Systems Of Equations



Multiple Cointegrating Relationships



Log-Odds And Logit Models: Using Grouped Data



Multinomial Models