In this paper, we study the finite sample accuracy of confidence intervals for index functional built via parametric bootstrap, in the case of inequality indices. To estimate the parameters of the assumed parametric data generating distribution, we propose a Generalized Method of Moment estimator that targets the quantity of interest, namely the considered inequality index. Its primary advantage is that the scale parameter does not need to be estimated to perform parametric bootstrap, since inequality measures are scale invariant. The very good finite sample coverages that are found in a simulation study suggest that this feature provides an advantage over the parametric bootstrap using the maximum likelihood estimator. We also find that overall, a parametric bootstrap provides more accurate inference than its non or semi-parametric counterparts, especially for heavy tailed income distributions.
Understanding and reducing the incidence of accidental bycatch, particularly for vulnerable species such as sharks, is a major challenge for contemporary fisheries management worldwide. Bycatch data, most often collected by at-sea observers during fishing trips, are clustered by trip and/or vessel and typically involve a large number of zero counts and very few positive counts. Though hurdle models are very popular for count data with excess zeros, models for clustered forms have received far less attention. Here we present a novel random-effects hurdle model for bycatch data that makes available accurate estimates of bycatch probabilities as well as other clusterspecific targets. These are essential for informing conservation and management decisions as well as for identifying bycatch hotspots, often considered the first step in attempting to protect endangered marine species. We validate our methodology through simulation and use it to analyze bycatch data on critically endangered hammerhead sharks from the U.S. National Marine Fisheries Service Pelagic Observer Program.
This study analyses the consistency between the 'control concept' of accounting which is used to identify the acquirer in business combinations and the 'economic substance over form concept' as overriding principle in international accounting standards. The 'economic substance' of transactions is measured by using similar methods as takeover target prediction which has been very successful in prior studies; a set of firm characteristics, most of them financial ratios is compared to the control assessment in over 8'000 business combinations of the last decade. The results suggest a good consistency when the control assessment in business combinations is rather simple, since primarily determined by voting rights. If the control assessment is more complex, as in business combinations where equity instruments are exchanged, or especially in reverse acquisitions, then the relations of firm characteristics deviate from expectations and suggest that the control judgment is not entirely corresponding to the 'economic substance' as indicated by firm characteristics.
In the first chapter, the problem of Bootstrap inference for the parameters of a GLMM is addressed. We formulate a bootstrapping strategy consisting on the random weighting of the contributions to the Joint Likelihood of Outcomes and Random Effects. Using the Laplace Approximation method for integrals on this function, yields a Random Weighted Log-Likelihood that produces the desired bootstrap replicates after optimization. In order to assess the properties of this procedure, that we name Random Weighted Likelihood Bootstrap (RWLB), we compare analytically their resulting EE to those of the Generalized Cluster Bootstrap for Gaussian LMM and conduct simulation studies both in a LMM and Mixed Logit regression contexts. The second chapter explores adaptations of the RWLB to the estimation of the uncertainty in prediction of random effects in a GLMM, as measured by the Mean Squared Error for the Predictors (MSEP).
One of the most important factors shaping world outcomes is where investment dollars are placed. In this regard, there is the rapidly growing area called sustainable investing where environmental, social, and corporate governance (ESG) measures are taken into account. With people interested in this type of investing rarely able to gain exposure to the area other than through a mutual fund, we study a cross section of U.S. mutual funds to assess the extent to which ESG measures are embedded in their portfolios. Our methodology makes heavy use of points on the nondominated surfaces of many tri-criterion portfolio selection problems in which sustainability is modeled, after risk and return, as a third criterion. With the mutual funds acting as a filter, the question is: How effective is the sustainable mutual fund industry in carrying out its charge? Our findings are that the industry has substantial leeway to increase the sustainability quotients of its portfolios at even no cost to risk and return, thus implying that the funds are unnecessarily falling short on the reasons why investors are investing in these funds in the first place.
