An Introduction to Modern Econometrics Using Stata
Integrating 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 method-of-moments 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 least-squares 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 method-of-moments estimation. The final chapters introduce panel-data analysis and discrete- and limited-dependent 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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2 Working with economic and
3 Organizing and handling economic
4 Linear regression
5 Specifying the functional form
6 Regression with noniid errors
7 Regression with indicator variables
1990q1 1992q3 sal
8 Instrumentalvariables estimators
_cons 2SLS autocorrelation Cntrl Coef coefficients compute Conf consistent estimates constant term correlated dataset define df MS Model discussed display distribution disturbance process do-file e(sample econometrics endogenous regressors equation explanatory F R-squared factors FGLS format function heteroskedasticity homoskedasticity housing price indicator variables instance instruments integer Interval ivreg2 lagged large-sample likelihood-ratio 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 P-value panel data parameters popsize population predicted values Prob probit probit model R-squared R-squared Adj R-squared R-squared 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 time-series variable name Variable Obs Mean variance varlist vector Wald test zero zero-conditional-mean assumption