DOC: add warning about zero scores

pyblp/results/economy_results.py

@@ -1321,7 +1321,9 @@ def compute_micro_scores(
         `list`
             Scores :math:`\mathscr{S}_n`. The list is in the same order as :attr:`ProblemResults.theta`. Each element
             of the list is an array of scores for the corresponding parameter. The array is in the same order as
-            observations appear in the ``micro_data``.
+            observations appear in the ``micro_data``. Note that it is possible for parameters in
+            :attr:`ProblemResults.theta` to mechanically have zero scores, for example if they are on a constant
+            demographic.
 
             Taking the mean of a parameter's scores delivers the observed ``value`` for an optimal
             :class:`MicroMoment` that matches the score for that parameter.
@@ -1409,7 +1411,9 @@ def compute_agent_scores(
             Scores :math:`\mathscr{S}_n`. The list is in the same order as :attr:`ProblemResults.theta`. Each element
             of the list is a mapping from market IDs supported by the ``dataset`` to an array of scores for the
             corresponding parameter and market. The array's dimensions correspond to the dimensions of the weights
-            returned by ``compute_weights`` passed to ``dataset``.
+            returned by ``compute_weights`` passed to ``dataset``. Note that it is possible for parameters in
+            :attr:`ProblemResults.theta` to mechanically have zero scores, for example if they are on a constant
+            demographic.
 
             To build an optimal :class:`MicroMoment` that matches the score for a parameter, ``compute_values``
             in its single :class:`MicroPart` should select the array corresponding to that parameter and the requested