Applied Econometrics with RR is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research. |
Contents
| 1 | |
Basics | 17 |
Linear Regression | 55 |
Diagnostics and Alternative Methods of Regression | 93 |
Models of Microeconometrics | 120 |
Time Series | 151 |
Programming Your Own Analysis | 183 |
References | 201 |
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Common terms and phrases
analysis anova Applied Econometrics argument ARIMA autocorrelation Baltagi basic binary binomial bootstrap called coefficients computed containing corresponding covariance matrix data frame data set default dependent variable Development Core Team diagnostic econometrics equation Error t value Estimate Std ethnicity example experience F statistic factor filter fitted values fitting function formula further GLMs graphics Hence heteroskedasticity I(experience^2 illustrate implemented income Intercept jour_lm journals data Kleiber least squares linear models linear regression linear regression model log(citeprice log(price/citations log(subs log(wage logarithms logit mean Median methods Microsoft Windows objects observations output p-value package panel parameter plot Poisson Poisson regression prediction prediction intervals price per citation probit provides quantile regression regressors residuals sample scatterplot specification Springer Science+Business Media standard errors structural change subset summary typically value Pr(>|t variance vector wage yields Zeileis


