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How can I calculate the pseudo-R² for xtprobit?

Question:

I estimated a random-effects probit model using xtprobit. A referee asks for a goodness-of-fit measure (some pseudo-R², or so). Although I do not see what we can learn from reporting such a number [...], I consider the damage from including it into the table of results to be minimal compared to the damage from trying to convince the referee. Anyway, I cannot find a goodness-of-fit measure in my output. [...] Where is the pseudo-R² for xtprobit, or how can I calculate the number from information given in the output?

Answer:

xtprobit is one of those models for which the log likelihood would be zero if the fit were perfect, so we can just scale the log-likelihood value of your model so that 1 corresponds to a log likelihood of 0 and 0 corresponds to the log likelihood of the constant-only model.

We can get the log likelihood of the constant-only model by typing

So let’s pretend that

        LL_o  =  −35.670226      (constant-only model)
        LL_f  =  −25.767073      (full model)
        LL_p  =    0.0           (perfect model)

All we need to do is scale the above so LL_0 corresponds to 0 and LL_p corresponds to 1.

        Pseudo R2 = (35.670226 − 25.767073)/35.670226
                  = .2776

You can see the Methods and Formulas for [R] maximize for a justification of the above formula.

Not too much strikes me wrong with the above, and I recommend you use it. If I were asked to criticize the above, I would point out that the perfect model leaves no room for a random effect (the random effect must be zero), and so perhaps the pseudo-R² value calculated is too low in some sense. This does not really bother me; you are just looking for a value to reflect, in some vague sense, how well you have fit the data, and the above calculation certainly does so in a reasonable way.

Be careful when obtaining the log likelihood for the constant-only model that you fit the model on the same estimation subsample on which you fitted the full model. Remember, Stata drops observations in which variables have missing values and, in the constant-only model, you are not specifying those variables. Probably the safest thing to do is refit the full model and then fit the constant-only model if e(sample).