In many economic settings, the variable of interest is often a fraction or a proportion, being defined only on the unit interval. The bounded nature of such variables and, in some cases, the possibility of nontrivial probability mass accumulating at one or both boundaries raise some interesting estimation and inference issues. In this paper we (i) provide a comprehensive survey of the main alternative models and estimation methods suitable to deal with fractional response variables, (ii) propose a full testing methodology to assess the validity of the assumptions required by each alternative estimator and (iii) examine the finite-sample properties of most of the estimators and tests discussed through an extensive Monte Carlo study. An application concerning corporate capital structure choices is also provided.

The present article discusses alternative regression models and estimation methods for dealing with multivariate fractional response variables. Both conditional mean models, estimable by nonlinear least squares and quasi-maximum likelihood, and fully parametric models (Dirichlet and Dirichlet-multinomial), estimable by maximum likelihood, are considered.In contrast to previous papers but similarly to the univariate case, a new parameterization is proposed here for the parametric models, which allows the same specification of the conditional mean of interest to be used in all models, irrespective of the specific functional form adopted for it. The text also discusses at some length the specification analysis of fractional regression models, proposing several tests that can be performed through artificial regressions. Finally, an extensive Monte Carlo study evaluates the finite sample properties of most of the estimators and tests considered.JEL classification code: C35.

Purpose This study aims to investigate the role of board gender diversity in explaining the effects of board members’ political connections on banking performance in the Eurozone. Design/methodology/approach This paper analyses panel data on 83 banks supervised by the European Central Bank (ECB) for the period 2013–2017, using a generalized moment method-type estimation methodology. Findings Results suggest that when gender diversity is high, there is a U-shaped nonlinear relationship between political connections and banking performance. Empirical evidence also indicates that differentiating characteristics of women, such as greater ethical concern and risk aversion, help mitigate the negative effects of political connections on banking performance, safeguarding the institutions’ interests from the adverse effects of personal agendas. In addition, these results also suggest that a minimum of 14% of gender diversity can contribute to greater social justice and beneficial structural change. Research limitations/implications The period studied may not yet fully reflect the impact of the assessment of the board members’ suitability. Practical implications The paper contributes to the growing literature on political connections and gender diversity, providing greater insight into their role as determinants of banking performance. The study also suggests the benefits and possible limitations of the regulator’s two impositions – gender diversity quotas and members’ repute (members’ political connections). Originality/value The effect of gender diversity on the impact of board members’ political connections on banking performance has not been studied, as these relationships have not been analysed separately for banks directly supervised by the ECB.

This paper proposes a new conditional mean test to assess the validity of binary and fractional parametric regression models. The new test checks the joint significance of two simple functions of the fitted index and is based on a very flexible parametric generalization of the postulated model. A Monte Carlo study reveals a promising behaviour for the new test, which compares favourably with that of the well‐known RESET test as well as with tests where the alternative model is non‐parametric.