Model Quality Report in Business Statistics - Harvard ...

2.2.3 Parameters
The parameters which can be estimated in GES are:
(a) count (an estimate of domain size);
(b) total (equations (2.1) and (2.2));
(c) mean;
(d) ratio(equations (2.3) and (2.4)).
Within CLAN, the user needs to construct several macros to specify the estimation to be
undertaken, and at this stage it is possible to include arbitrary rational functions of totals, so
that purpose-built estimands can be constructed and their sampling variances calculated
explicitly within the package. GES allows only the four estimands described above, but in a
similar way the variances of linear combinations can be found afterwards outside the
package. In general however, this will require more expertise and effort than setting up the
appropriate macros in CLAN. The PC-CARP documentation suggests that it estimates
quantiles (with the appropriate variances) too, a facility not available in either GES or CLAN.
STATA and SUDAAN have:
(a) count;
(b) mean;
(c) total;
(d) ratio;
(e) regression parameters;
(f) Wald statistics;
(g) logistic regression parameters;
(h) quantiles;
and for STATA only
(i) arbitrary linear combinations of parameters.
Some of these are not currently widely used in business surveys, but there seems to be some
development in the field of estimating distributions, which will make the estimation of
quantiles more important, and the facility to produce estimates and variance estimates for
arbitrary linear combinations of parameters can be used to assist in the estimation of
variances of “complex” population parameters such as changes, index numbers and so on (see
chapter M3).
WesVar produces a similar range of statistics to STATA and SUDAAN, including arbitrary
linear and non-linear combinations of statistics. The sampling variances of the non-linear
statistics can be found because WesVar relies on replication methods.
Of particular interest in repeating business surveys are estimates of movement or change.
Where the units are exactly common between two periods (almost never true even if the
design is set up in this way because of differential non-response), then any of the packages
here can be used to estimate the movement by including the responses for different periods as
two survey variables. When the units are not the same, then it becomes very challenging to
produce an appropriate estimate of change and its variance. Within CLAN this can be
achieved by including the union of the two samples as the sample, and specifying the
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