The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Economics students new to both Econometrics and R may find the introduction to both challenging. However, if their text is “Introductory Econometrics: A Modern Approach, 7e” by Jeffrey M. Wooldridge, they are in luck!
The wooldridge
package aims to lighten the task by
easily loading any data set from the text. The package contains full
documentation for each set and all data have been compressed to a
fraction of their original size. Just install the package, load it, and
call the data you wish to work with.
But wait…there’s more! A vignette, Introductory Econometrics Examples:sparkles:, illustrates solutions to examples from each chapter of the text, offering a relevant introduction to econometric modelling with R. The vignette also includes an Appendix of R resources, such as Using R for Introductory Econometrics by Florian Heiss.
Note: All data sets are from the 7th edition
(Wooldridge 2020, ISBN-13: 978-1-337-55886-0
), which is
compatible with all other editions.
One can Install wooldridge
directly from Github or The
Comprehensive R Archive Network (CRAN). Recent
additions to the data set has bumped the dependency up to R
>= 3.5.0.
# 7th edition on CRAN
install.packages("wooldridge")
# 7th edition
remotes::install_github("JustinMShea/wooldridge")
It’s always recommended that one read supporting documentation for
data sets of interest. This becomes trivial with the
wooldridge
package:
?wage1
Documentation includes Wooldridge’s original source, variable descriptions, as well as page numbers in the referenced text. Some sets even contain additional notes suggesting related research projects or exploration.
Load the wooldridge
package and use the
data()
function to load the desired data set. Data set
names match those in the text. Once loaded into the working environment,
modeling data is quick and easy, leaving learners with more time to
focus on interpretation of results and general diagnostics.
library(wooldridge)
data("wage1")
wageModel <- lm(lwage ~ educ + exper + tenure, data = wage1)
summary(wageModel)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.