Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
In a transformation model yt=c[a(xt,β),ut], where the errors ut are i.i.d. and independent of the explanatory variables xt, the parameters can be estimated by a pseudo‐maximum likelihood (PML) method, that is, by using a misspecified distribution of the errors, but the PML estimator of β is in general not consistent. We explain in this paper how to nest the initial model in an identified augmented model with more parameters in order to derive consistent PML estimators of appropriate functions of parameter β. The usefulness of the consistency result is illustrated by examples of systems of nonlinear equations, conditionally heteroscedastic models, stochastic volatility, or models with spatial interactions.