Wooldridge Source: Jiyoung Kwon, a former doctoral student in economics at MSU, kindly provided these data, which she obtained from the Domestic Airline Fares Consumer Report by the U.S. Department of Transportation. Data loads lazily.
data('airfare')A data.frame with 4596 observations on 14 variables:
year: 1997, 1998, 1999, 2000
id: route identifier
dist: distance, in miles
passen: avg. passengers per day
fare: avg. one-way fare, $
bmktshr: fraction market, biggest carrier
ldist: log(distance)
y98: =1 if year == 1998
y99: =1 if year == 1999
y00: =1 if year == 2000
lfare: log(fare)
ldistsq: ldist^2
concen: = bmktshr
lpassen: log(passen)
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
This data set nicely illustrates the different estimates obtained when applying pooled OLS, random effects, and fixed effects.
Used in Text: pages 506-507, 581
str(airfare)
#> 'data.frame': 4596 obs. of 14 variables:
#> $ year : int 1997 1998 1999 2000 1997 1998 1999 2000 1997 1998 ...
#> $ id : int 1 1 1 1 2 2 2 2 3 3 ...
#> $ dist : int 528 528 528 528 861 861 861 861 852 852 ...
#> $ passen : int 152 265 336 298 282 178 204 190 241 253 ...
#> $ fare : int 106 106 113 123 104 105 115 129 207 188 ...
#> $ bmktshr: num 0.839 0.813 0.826 0.861 0.58 ...
#> $ ldist : num 6.27 6.27 6.27 6.27 6.76 ...
#> $ y98 : int 0 1 0 0 0 1 0 0 0 1 ...
#> $ y99 : int 0 0 1 0 0 0 1 0 0 0 ...
#> $ y00 : int 0 0 0 1 0 0 0 1 0 0 ...
#> $ lfare : num 4.66 4.66 4.73 4.81 4.64 ...
#> $ ldistsq: num 39.3 39.3 39.3 39.3 45.7 ...
#> $ concen : num 0.839 0.813 0.826 0.861 0.58 ...
#> $ lpassen: num 5.02 5.58 5.82 5.7 5.64 ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"