PurposeTo contrast the different factors that can determine the level of debt of firms by means of panel data methodology.Design/methodology/approachThe variables used in the study are: size, generated resources, level of warrants, debt cost, growth opportunities, and reputation. Six hypotheses are considered.FindingsThe results obtained suggest that the stated variables, other than reputation, can be considered to be explanatory variables of firm debt level. Using within‐groups estimation and generalized least squares, the results suggest that the behavior of the sample throughout the study period is consistent with the fixed effects approach, in which the specific characteristics of each firm remain constant throughout time. Moreover, with respect to the six considered hypotheses, the analysis shows the influence of all stated variables except reputation on the leverage.Originality/valueAdds to the body of research that has focused on the analysis of the financial decisions of the firm, with the level of debt appearing as a relevant factor in explaining the relationship between investment and financing decisions.

This paper introduces some new elements to measure the skewness of a probability distribution, suggesting that a given distribution can have both positive and negative skewness, depending on the centred sub‐interval of the support set being observed. A skewness function for positive reals is defined, from which a bivariate index of positive–negative skewness is obtained. Certain interesting properties of this new index are studied, and they are also obtained for some common discrete distributions. We show the advantages of their use as a complement to the information derived by traditional measures of skewness.

Statistical meta-analysis is mostly carried out with the help of the random effect normal model, including the case of discrete random variables. We argue that the normal approximation is not always able to adequately capture the underlying uncertainty of the original discrete data. Furthermore, when we examine the influence of the prior distributions considered, in the presence of rare events, the results from this approximation can be very poor. In order to assess the robustness of the quantities of interest in meta-analysis with respect to the choice of priors, this paper proposes an alternative Bayesian model for binomial random variables with several zero responses. Particular attention is paid to the coherence between the prior distributions of the study model parameters and the meta-parameter. Thus, our method introduces a simple way to examine the sensitivity of these quantities to the structure dependence selected for study. For illustrative purposes, an example with real data is analysed, using the proposed Bayesian meta-analysis model for binomial sparse data. Copyright © 2016 John Wiley & Sons, Ltd.

Linear regression models are often used to represent the cost and effectiveness of medical treatment. The covariates used may include sociodemographic variables, such as age, gender or race; clinical variables, such as initial health status, years of treatment or the existence of concomitant illnesses; and a binary variable indicating the treatment received. However, most studies estimate only one model, which usually includes all the covariates. This procedure ignores the question of uncertainty in model selection. In this paper, we examine four alternative Bayesian variable selection methods that have been proposed. In this analysis, we estimate the inclusion probability of each covariate in the real model conditional on the data. Variable selection can be useful for estimating incremental effectiveness and incremental cost, through Bayesian model averaging, as well as for subgroup analysis.

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