Wooldridge Source: Blundell, R., A. Duncan, and K. Pendakur (1998), “Semiparametric Estimation and Consumer Demand,” Journal of Applied Econometrics 13, 435-461. I obtained these data from the Journal of Applied Econometrics data archive at http://qed.econ.queensu.ca/jae/. Data loads lazily.
data('expendshares')
A data.frame with 1519 observations on 13 variables:
sfood: share of food expenditures (out of total)
sfuel: share of fuel expenditures
sclothes: share of clothing expenditures
salcohol: share of alcohol expenditures
stransport: share of transportation expenditures
sother: share of other expenditures
totexpend: total expenditure, British pounds per week
income: family income, British pounds per week
age: age of household head
kids: number of children: 1 or 2
ltotexpend: log(totexpend)
lincome: log(income)
agesq: age^2
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
The dependent variables in this data set – the expenditure shares – are necessarily bounded between zero and one. The linear model is at best an approximation, but the usual IV estimator likely gives good estimates of the average partial effects.
Used in Text: pages 581-582
#> 'data.frame': 1519 obs. of 13 variables: #> $ sfood : num 0.427 0.374 0.194 0.444 0.333 ... #> $ sfuel : num 0.1342 0.1686 0.4056 0.1258 0.0824 ... #> $ sclothes : num 0 0.0091 0.0012 0.0539 0.0399 ... #> $ salcohol : num 0.0106 0.0825 0.0513 0.0397 0.1571 ... #> $ stransport: num 0.1458 0.1215 0.2063 0.0652 0.2403 ... #> $ sother : num 0.282 0.244 0.141 0.272 0.147 ... #> $ totexpend : int 50 90 180 80 90 70 140 50 100 90 ... #> $ income : int 130 150 230 100 100 70 190 100 260 110 ... #> $ age : int 25 39 47 33 31 24 46 25 30 41 ... #> $ kids : int 2 2 2 2 1 1 1 1 1 1 ... #> $ ltotexpend: num 3.91 4.5 5.19 4.38 4.5 ... #> $ lincome : num 4.87 5.01 5.44 4.61 4.61 ... #> $ agesq : int 625 1521 2209 1089 961 576 2116 625 900 1681 ... #> - attr(*, "time.stamp")= chr "22 Jan 2013 14:09"