Using microSynth is easy when the models used to calculate weights are feasible. But as more variables are used for matching, especially when data is scarce or variables are sparse, the risk of an infeasible model increases. Below is a quick guide to how to troubleshoot model feasiblity issues.
Model infeasibility becomes increasingly likely when:
As there are multiple causes of model infeasibility, there is an equally broad range of responses.
If a model is found to be infeasible, the problem may trace back to matching variable specification. We recommend the following diagnostic steps:
table()) to check for sparseness.
Sparse variables are difficult to match on without large sample
match.covar.min), or aggregate
time-variant variables before matching.
When attempts to match on a sparse variable cause model infeasibility, there are several solutions:
if the variable is time-variant, move it from
match.out.min a vector of
variable names, and specify the aggregation periods using
If varying the specification of matching variables is not satisfactory, the user can set the parameters microSynth() uses for the calculation of weights.
max.mse may be raised. This relaxes the constraint
governing matches for variables passed to
which correspond to parameters from the
and govern the calculation of weights.
By default microSynth() attempts to calculate weights using simple
methods. But because these are not always sufficient to produce a
feasible model, two arguments,
use.backup, specify how microsynth should find and use less
restrictive backup models. The two arguments do not interact and can be
check.feas = TRUE will search for a single model that
yields satisfactory constraints for all purposes: estimating main
weights, permutation weights, and jackknife residuals. The same model
will be used for all purposes.
use.backup = TRUE will calculate the main weights
without checking for feasibility, but if weights appear to be poor
(i.e., they do not satisfy the max.mse condition), then weights will be
re-calculated using another model. This way, different backup models may
be used for different purposes (i.e., for estimating main weights,
permutation weights, and jackknife residuals).