An Introduction to Modern Econometrics Using StataIntegrating a contemporary approach to econometrics with the powerful computational tools offered by Stata, An Introduction to Modern Econometrics Using Stata focuses on the role of methodofmoments estimators, hypothesis testing, and specification analysis and provides practical examples that show how the theories are applied to real data sets using Stata. As an expert in Stata, the author successfully guides readers from the basic elements of Stata to the core econometric topics. He first describes the fundamental components needed to effectively use Stata. The book then covers the multiple linear regression model, linear and nonlinear Wald tests, constrained leastsquares estimation, Lagrange multiplier tests, and hypothesis testing of nonnested models. Subsequent chapters center on the consequences of failures of the linear regression model's assumptions. The book also examines indicator variables, interaction effects, weak instruments, underidentification, and generalized methodofmoments estimation. The final chapters introduce paneldata analysis and discrete and limiteddependent variables and the two appendices discuss how to import data into Stata and Stata programming. Presenting many of the econometric theories used in modern empirical research, this introduction illustrates how to apply these concepts using Stata. The book serves both as a supplementary text for undergraduate and graduate students and as a clear guide for economists and financial analysts. 
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Contents
2 Working with economic and  7 
3 Organizing and handling economic  43 
4 Linear regression  69 
5 Specifying the functional form  115 
6 Regression with noniid errors  133 
7 Regression with indicator variables  161 
1990q1 1992q3 sal  178 
ci  183 
9 Paneldata models  219 
10 15 20 10 15  244 
10 Models of discrete and limited  247 
A Getting the data into Stata  277 
B The basics of Stata programming  289 
321  
Author index  329 
8 Instrumentalvariables estimators  185 
Common terms and phrases
_cons 2SLS autocorrelation Cntrl Coef coefficients compute Conf consistent estimates constant term correlated dataset define df MS Model discussed display distribution disturbance process dofile e(sample econometrics endogenous regressors equation explanatory F Rsquared factors FGLS format function heteroskedasticity homoskedasticity housing price indicator variables instance instruments integer Interval ivreg2 lagged largesample likelihoodratio test linear regression logit lwage macro marginal effects Mata matrix measure medage missing values null hypothesis Number of obs Obs Mean Std observations OLS estimator option Pvalue panel data parameters popsize population predicted values Prob probit probit model Rsquared Rsquared Adj Rsquared Rsquared Root MSE regression estimates regression model regressors residuals response variable robust sample scalar semean Source SS df specify standard errors Stata commands string variables summarize syntax tenure timeseries variable name Variable Obs Mean variance varlist vector Wald test zero zeroconditionalmean assumption