Econometrics: A Modern Introduction: International Edition

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.

OVERVIEW

Part I - The Linear Regression Model

1.  What is Econometrics?

2. Choosing Estimators: Intuition and Monte Carlo Methods

3.  Linear Estimators and the Gauss—Markov Theorem

4.  Blue Estimators for the Slope and Intercept of a Straight Line

5.  Residuals

6.   Multiple Regression

Part II - Specification and Hypothesis Testing

7.  Testing Single Hypotheses in Regression Models

8.  Superfluous and Omitted Variables, Multicollinearity and Binary Variables

9.  Testing Multiple Hypotheses

Part III - Further Topics in Regression

10.  Heteroskedastic Disturbances

11.  Autoregressive Disturbances

12.  Large Sample Properties Of Estimators:  Consistency and Asymptotic Efficiency

13.  Instrumental Variables Estimation

14.  Systems of Equations

15.  Randomized Experiments and Natural Experiments

16.  Analyzing Panel Data

17.  Forecasting

18.  Stochastically Trending Variables

19.  Logit and Probit Models: Truncated and Censored Samples

Statistical Appendix                                                                                      

WEB EXTENSION 1            USING CALCULUS AND ALGEBRA FOR THE SIMPLEST CASE: n = 3

WEB EXTENSION 2            LOCAL AVERAGE TREATMENT EFFECTS

WEB EXTENSION 3            GENERALIZED METHOD OF MOMENTS ESTIMATORS AND IDENTIFICATION

WEB EXTENSION 4            MAXIMUM LIKELIHOOD ESTIMATION

WEB EXTENSION 5            ESTIMATORS FOR SYSTEMS OF EQUATIONS

WEB EXTENSION 6            MULTIPLE COINTEGRATING RELATIONSHIPS

WEB EXTENSION 7            LOG-ODDS AND LOGIT MODELS: USING GROUPED DATA

WEB EXTENSION 8            MULTINOMIAL MODELS

• 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.  Because many students do not understand sampling distributions, Econometrics: A Modern Introduction begins with a Monte Carlo exercise that compares students’ own estimators of the slope of a line through the origin. The exercise conditions students to think like econometricians right from the start by focusing their attention on how estimators perform across repeated samples.
• Regression’s Greatest Hits features, which present a series of exemplary econometric analyses, help students make a clear connection between econometrics and economics. Early Hits help students make the leap from the theoretical economics they have studied to the econometrically convenient linear forms in which econometricians usually cast economic theories. Later Hits illustrate how newly introduced techniques have been used to obtain practical, profound, or amusing results, and show students how important economic knowledge is grounded in econometric research.
• Econometrics: A Modern Introduction  keeps economic behavior in the foreground by focusing on real-life applications as it uses theory to quantify economic relationships.
• Econometrics: A Modern Introduction  can be used with both introductory and advanced students possessing the prerequisite economic and statistic theoretical background. 
• A Monte Carlo simulation tutorial on the textbook Web site takes students through the choices they must make to build their Monte Carlo models, and then presents them with the Monte Carlo results.
• Econometrics: A Modern Introduction returns to Monte Carlo analyses to introduce heteroskedasticity, errors in variables, and consistency. Programs for these exercises are also on the Web site.