Wooldridge Source: J. Mullahy (1997), “Instrumental-Variable Estimation of Count Data Models: Applications to Models of Cigarette Smoking Behavior,” Review of Economics and Statistics 79, 596-593. Professor Mullahy kindly provided the data. Data loads lazily.
data('smoke')A data.frame with 807 observations on 10 variables:
educ: years of schooling
cigpric: state cig. price, cents/pack
white: =1 if white
age: in years
income: annual income, $
cigs: cigs. smoked per day
restaurn: =1 if rest. smk. restrictions
lincome: log(income)
agesq: age^2
lcigpric: log(cigprice)
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
If you want to do a “fancy” IV version of Computer Exercise C16.1, you could estimate a reduced form count model for cigs using the Poisson regression methods in Section 17.3, and then use the fitted values as an IV for cigs. Presumably, this would be for a fairly advanced class.
Used in Text: pages 183, 288-289, 298, 301, 578, 627
str(smoke)
#> 'data.frame': 807 obs. of 10 variables:
#> $ educ : num 16 16 12 13.5 10 6 12 15 12 12 ...
#> $ cigpric : num 60.5 57.9 57.7 57.9 58.3 ...
#> $ white : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ age : int 46 40 58 30 17 86 35 48 48 31 ...
#> $ income : int 20000 30000 30000 20000 20000 6500 20000 30000 20000 20000 ...
#> $ cigs : int 0 0 3 0 0 0 0 0 0 0 ...
#> $ restaurn: int 0 0 0 0 0 0 0 0 0 0 ...
#> $ lincome : num 9.9 10.3 10.3 9.9 9.9 ...
#> $ agesq : int 2116 1600 3364 900 289 7396 1225 2304 2304 961 ...
#> $ lcigpric: num 4.1 4.06 4.05 4.06 4.07 ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"