Essentials of Applied Econometrics
Univ of California Press, Nov 8, 2016 - Business & Economics - 240 pages
"Essentials of Applied Econometrics gives students and professionals the tools they need to do econometric analysis in a world in which more data surround us every day and in which econometrics is put to a diversity of uses. Vivid examples and data from a variety of real-world sources are used to teach best practices and state-of-the-art techniques. This book differs from traditional textbooks that assume the only goal of econometrics is to estimate causal effects and that confound sampling theory with causal analysis. It begins with sampling theory - how to use a sample to make inferences about a whole population. Then, in the last two chapters, it addresses causality as a distinct topic. In between, it covers the gambit of topics essential for doing econometrics, including properties of estimators, hypothesis testing, nonlinear relationships, heteroskedasticity, serial correlation, and sampling bias. This book covers essential theory but with an emphasis on the best practices for estimating econometric models. The text is succinct, written for students and professionals interested in continuing their econometric education"--Provided by publisher.
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Introduction to Econometrics
Generalizing from a Sample
Properties of Our Estimators
Correlated Errors Assumption CR3
Sample Selection Bias Assumption
Critical Values for Commonly
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20 schools academic performance index assumptions autocorrelation average bias capita causation central limit theorem Chapter confidence intervals consumption critical value demand dependent variable earnings econometric model econometricians economic economists effect Elementary endogeneity problem endogenous error term Estimated Coefficient example exogenous experience figure FLE and API formula free lunch gender heteroskedasticity homoskedastic households hypothesis tests income increase instrument intercept lagged least-squares linear look mean measure migration Mincer multiple regression nonlinear normally distributed null hypothesis observations OLS estimator OLS regression omitted variables outcome panel data parameter people’s population model predict API production function random sample randomly regression equation regression line regression model reject the null relationship remittances residuals right-hand-side variables simple regression model slope standard error statistic Table test scores theory there’s time-series tion unbiased variance variation White’s whole population workforce zero