Wooldridge Source: Collected by Stephanie Balys, a former MSU undergraduate, from the New York Stock Exchange and Compustat. Data loads lazily.
data('return')
A data.frame with 142 observations on 12 variables:
roe: return on equity, 1990
rok: return on capital, 1990
dkr: debt/capital, 1990
eps: earnings per share, 1990
netinc: net income, 1990 (mills.)
sp90: stock price, end 1990
sp94: stock price, end 1994
salary: CEO salary, 1990 (thous.)
return: percent change s.p., 90-94
lsalary: log(salary)
lsp90: log(sp90)
lnetinc: log(netinc)
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
More can be done with this data set. Recently, I discovered that lsp90 does appear to predict return (and the log of the 1990 stock price works better than sp90). I am a little suspicious, but you could use the negative coefficient on lsp90 to illustrate “reversion to the mean.”
Used in Text: page 162-163
#> 'data.frame': 142 obs. of 12 variables: #> $ roe : num 18.7 1.6 4.9 11.1 5.6 ... #> $ rok : num 17.4 2.4 4.6 8.6 4.5 ... #> $ dkr : num 4 27.3 36.8 46.4 36.2 ... #> $ eps : num 48.1 -85.3 -44.1 192.4 -60.4 ... #> $ netinc : int 1144 35 127 367 214 118 175 1692 157 315 ... #> $ sp90 : num 59.4 47.9 39 61.2 58 ... #> $ sp94 : num 47 43.5 72.6 142 53.2 ... #> $ salary : int 1090 1923 1012 579 600 735 994 1227 913 733 ... #> $ return : num -20.84 -9.14 86.22 131.84 -8.19 ... #> $ lsalary: num 6.99 7.56 6.92 6.36 6.4 ... #> $ lsp90 : num 4.08 3.87 3.66 4.11 4.06 ... #> $ lnetinc: num 7.04 3.56 4.84 5.91 5.37 ... #> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"