María Concepción Verona Martel

Determinant factors of leverage: An empirical analysis of Spanish corporations

Purpose – To contrast the different factors that can determine the level of debt of firms by means of panel data methodology. Design/methodology/approach – The variables used in the study are: size, generated resources, level of warrants, debt cost, growth opportunities, and reputation. Six hypotheses are considered. Findings – The 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/value – Adds 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.

Bayesian Variable Selection in Cost-Effectiveness Analysis

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.

Bayesian robustness in meta-analysis for studies with zero responses.

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

Bayesian Approach to Evidence Synthesis

We briefly present the advantages and opportunities available to umbrella reviews from the use of Bayesian techniques while taking into account that the concerns commonly arising in Bayesian meta-analysis procedures are also present in umbrella reviews. This is the case, for example, of sparse data, for which the hierarchical logit-normal model can give very poor results. An additional concern in this context is that of the choice of noninformative priors, which can lead to a significant variation in the final conclusions drawn. Accordingly, this chapter highlights the potential for Bayesian approaches in umbrella reviews, overviews of reviews, and meta-epidemiologic studies while acknowledging their limitations and complexities.

A Bayesian sensitivity study of risk difference in the meta-analysis of binary outcomes from sparse data

In most cases, including those of discrete random variables, statistical meta-analysis is carried out using the normal random effect model. The authors argue that normal approximation does not always properly reflect the underlying uncertainty of the original discrete data. Furthermore, in the presence of rare events the results from this approximation can be very poor. This review proposes a Bayesian meta-analysis to address binary outcomes from sparse data and also introduces a simple way to examine the sensitivity of the quantities of interest in the meta-analysis with respect to the structure dependence selected. The findings suggest that for binary outcomes data it is possible to develop a Bayesian procedure, which can be directly applied to sparse data without ad hoc corrections. By choosing a suitable class of linking distributions, the authors found that a Bayesian robustness study can be easily implemented. For illustrative purposes, an example with real data is analyzed using the proposed Bayesian meta-analysis for binomial sparse data.