Wooldridge Source: This is a data set I created many years ago intended as an update to the files JTRAIN2 and JTRAIN3. While the data were partly generated by me, the data attributes are similar to data sets used to evaluate job training programs. Data loads lazily.
data('jtrain98')
A data.frame with 1130 observations on 10 variables:
train: =1 if in job training
age: in years
educ: years of schooling
black: =1 if black
hisp: =1 if Hispanic
married: =1 if married
earn96: earnings in 1996, $1000s
unem96: =1 if unemployed all of 1995
earn98: earnings in 1998, $1000s
unem98: =1 if unemployed all of 1998
http://www.cengage.com/c/introductory-econometrics-a-modern-approach-7e-wooldridge
The response variables, earn98 and unem98, both have discreteness: the former is a corner solutions (takes on the value zero and then a range of strictly positive values) and the latter is binary. One could use these in an exercise using methods in Chapter 17. unem98 can be used in a probit or logit model, earn98 in a Tobit model, or in Poisson regression (without assuming, of course, that the Poisson distribution is correct).
Used in Text: 101-102, 248, 601
#> 'data.frame': 1130 obs. of 10 variables: #> $ train : int 0 0 1 1 0 1 0 1 0 1 ... #> $ age : int 37 30 27 33 22 23 32 22 19 21 ... #> $ educ : int 11 12 11 8 9 12 11 16 9 13 ... #> $ black : int 1 1 1 1 1 1 1 1 1 1 ... #> $ hisp : int 0 0 0 0 0 0 0 0 0 0 ... #> $ married: int 1 0 0 0 0 0 0 0 0 0 ... #> $ earn96 : num 0 0 0 0 0 0 0 0 0 0 ... #> $ unem96 : int 1 1 1 1 1 1 1 1 1 1 ... #> $ earn98 : num 1.62 0 0 9.07 4.02 ... #> $ unem98 : int 0 1 1 0 0 0 0 0 1 0 ... #> - attr(*, "time.stamp")= chr "21 Dec 2018 17:08" #> - attr(*, "label.table")= list() #> - attr(*, "expansion.fields")= list() #> - attr(*, "byteorder")= chr "LSF" #> - attr(*, "orig.dim")= int [1:2] 1130 10