* Load the Theil textile data set with the DEMO command.
?DEMO
* COMPUTING THE POWER OF A TEST
* For an OLS regression, calculate a power function for testing the 
* null hypothesis that the coefficient on log income is one against 
* a 2-sided alternative.
sample 1 17
* Convert data to base 10 logs as implemented by Theil.
genr lconsume=log(consume)/2.3026
genr lincome=log(income)/2.3026
genr lprice=log(price)/2.3026
* Run OLS and save the coefficients. Assume these are the true coefficients.
ols lconsume lincome lprice / coef=beta 
* Assume the OLS sigma**2 is the true value.
gen1 sig2=$sig2
sample 1 1
* Set a 5% significance level.
genr alpha=.05
* Find the critical value for the rejection of the null hypothesis.
distrib alpha / type=f df1=1 df2=14 inverse
genr cr=$crit
* Dimension room for 11 values of the power function.
dim  p 11 b 11
* Turn off the DO-loop echoing of commands.
set nodoecho nooutput
do #=1,11
* Let the "true" income coefficient (beta:1) vary between .5 and 1.5.
* The other coefficients are unchanged.
genr beta:1=.4+.1*#
* Store the hypothesised values of the income coefficient in the vector b.
genr b:#=beta:1
sample 1 17
* Calculate OLS regression results using the "true" beta and sig2.
* These results are then used as input for the TEST command that follows.
ols lconsume lincome lprice / incoef=beta insig2=sig2
test lincome=1
* Temporary variables available from the TEST command are the
* degrees of freedom saved in $df1 and $df2 and the chi-square 
* test statistic saved in $chi. The latter is used as the
* non-centrality parameter.
sample 1 1
distrib cr / type=f df1=$df1 df2=$df2 c=$chi cdf=cdf
* p is the power (the probability of rejecting the false null hypothesis).
genr p:#=1-cdf
endo
* List the power function
sample 1 11 
print b p 
graph p b / lineonly
stop