options crt;  ? 7e3
in ukelec;
smpl 1,42;
precip = 1/mc6;
ols kwh c inc precip gas6 cap;  ? (a)
w = sqrt(cust);
dot kwh c inc precip gas6 cap;
  genr .w = .*w;  ? create weighted variables kwhw, cw, ... capw
enddot;
ols kwhw cw incw precipw gas6w capw;
ols(robust) kwh c inc precip gas6 cap;
ols(weight=cust) kwh c inc precip gas6 cap;
ols(weight=cust,unnorm) kwh c inc precip gas6 cap;  ? (b)
?
? Here is a standard TSP technique for extending the dataset to
? create artificial observations for evaluating elasticities.
?
? create a fake 43rd data observation for evaluating elasticities;
? also a fake 44th observation with the means
smpl 43,43;
 list vars inc mc6 gas6 cap kwh;
 read vars;  500 .5 8 .5 1171;
smpl 1,42;
 msd(noprint) vars;
smpl 44,44;
 unmake @mean vars;
list @rnms b1-b5;   ? rename the coefficients in the last regression
                    ? so that ANALYZ can distinguish them from the
                    ? variable names
param @rnms;  unmake @coef @rnms;    ? store coef values as PARAMs
frml einc incel = b2*inc/kwh;
frml epr prel = -b3*precip/kwh;  ? dKWH/dP = (dKWH/dPRECIP)*(dPRECIP/dP)
?
?  Always try to use ANALYZ to evaluate elasticities, because it
?  will also compute the standard error (based on the VCOV of the
?  estimated coefficients).
?
smpl 43,43;
  analyz einc epr;   ? (c) at specified values
smpl 44,44;
  analyz einc epr;   ? (c) at sample means
smpl 1,42;
lnx = log(kwh);
lnm = log(inc);
lnpe2 = log(mc6);
lnpg2 = log(gas6);
lnh = log(cap);
ols lnx c lnm lnpe2 lnpg2 lnh;
ols(weight=w,unnorm) lnx c lnm lnpe2 lnpg2 lnh;  ? (d)