Harvesting woody biomass for biofuel has become an important research topic. In Mississippi, feasibility of utilizing woody biomass for bioenergy lies in the willingness to harvest by non-industrial private forest (NIPF) landowners, who control 71% of forestlands. A mail survey of Mississippi NIPF landowners elicited preferences concerning utilizing logging residues for bioenergy. When presented with hypothetical situations that compared bioenergy utilization attributes along with those of standard harvesting practices, more landowners preferred the bioenergy scenarios, even when more money was offered for standard harvesting. Older landowners with larger landholdings were less likely to prefer bioenergy scenarios. Higher educated landowners who were financially motivated, concerned with climate change, and considered habitat management an important goal were more likely to prefer bioenergy scenarios over standard harvesting. Available markets for logging residues could increase NIPF harvest rates based solely on the different harvesting attributes, which should increase availability of feedstocks for producers.

Submitted by Secretaria Pós de Produção (tpp@vm.uff.br) on 2017-07-27T18:53:19Z No. of bitstreams: 1 D2014 - Thiago Graça Ramos.pdf: 589803 bytes, checksum: d74ab5e26ec9908670c7d3320d45fe61 (MD5) / Made available in DSpace on 2017-07-27T18:53:19Z (GMT). No. of bitstreams: 1 D2014 - Thiago Graça Ramos.pdf: 589803 bytes, checksum: d74ab5e26ec9908670c7d3320d45fe61 (MD5) / Esse estudo visa construir um modelo para identificar a forma ideal de transporte de carga no Brasil, para pequenas e médias empresas que contratam este tipo de serviço. O trabalho utilizou as técnicas DEA, preferência declarada e logito ordinal para avaliar as pequenas e médias empresas que contratam transporte de carga no Brasil, verificando os aspectos importantes para a tomada de decisão na contratação deste serviço. Inicialmente, aplicou-se a ferramenta DEA para classificar as eficiências em alta, média e baixa, utilizandose o resultado de tal classificação como a variável dependente do modelo logito ordinal. As variáveis independentes deste modelo foram as utilidades oriundas da preferência declarada e do modelo de MaxDiff, que avaliou características não pertencentes ao modelo de preferência declarada. A análise dos dados indicou que a migração do modo rodoviário para o ferroviário seria melhor para as empresas, já que o primeiro acaba sendo utilizado pela falta de opção pelo segundo. Outro importante resultado do estudo foi a indicação de que as empresas com produtos de maior valor agregado são mais eficientes. Por fim, o modelo indicou que o modo de operação a ser buscado pelas empresas de transporte de carga deve incluir segurança e rapidez na entrega, propiciando facilidade de acesso ao consumidor. / This paper aims to identify efficient businesses in daily freight transport and to evaluate the main aspects to picking and hiring a cargo transportation service. To make this evaluation, some techniques will be used, such as Data Envelopment Analysis, ordinal logit and revealed preference. By using the DEA technique, the efficiency will be ranked between high, medium and low, and this ranking will be the dependent variable of the ordinal logit model, and the independent variables of this model are derived from the utilities from the revealed preference model and the maxdiff model that evaluated some features that were not declared on the preference model. Data analysis indicated that the migration from road to rail would be better for companies since the first ends up being used by a lack of options for the second. Another important result was the indication that firms with higher value-added products are more efficient. Finally, the model indicated that the mode of operation being sought by cargo shipping companies should include safety and speed in delivery, providing easy access to the consumer.

This thesis deals with the development, implementation and application of statistical modeling techniques which can be employed in the analysis of credit ratings. Credit ratings are one of the most widely used measures of credit risk and are relevant for a wide array of financial market participants, from investors, as part of their investment decision process, to regulators and legislators as a means of measuring and limiting risk. The majority of credit ratings is produced by the "Big Three" credit rating agencies Standard & Poors', Moody's and Fitch. Especially in the light of the 2007-2009 financial crisis, these rating agencies have been strongly criticized for failing to assess risk accurately and for the lack of transparency in their rating methodology. However, they continue to maintain a powerful role as financial market participants and have a huge impact on the cost of funding. These points of criticism call for the development of modeling techniques that can 1) facilitate an understanding of the factors that drive the rating agencies' evaluations, 2) generate insights into the rating patterns that these agencies exhibit. This dissertation consists of three research articles. The first one focuses on variable selection and assessment of variable importance in accounting-based models of credit risk. The credit risk measure employed in the study is derived from credit ratings assigned by ratings agencies Standard & Poors' and Moody's. To deal with the lack of theoretical foundation specific to this type of models, state-of-the-art statistical methods are employed. Different models are compared based on a predictive criterion and model uncertainty is accounted for in a Bayesian setting. Parsimonious models are identified after applying the proposed techniques. The second paper proposes the class of multivariate ordinal regression models for the modeling of credit ratings. The model class is motivated by the fact that correlated ordinal data arises naturally in the context of credit ratings. From a methodological point of view, we extend existing model specifications in several directions by allowing, among others, for a flexible covariate dependent correlation structure between the continuous variables underlying the ordinal credit ratings. The estimation of the proposed models is performed using composite likelihood methods. Insights into the heterogeneity among the "Big Three" are gained when applying this model class to the multiple credit ratings dataset. A comprehensive simulation study on the performance of the estimators is provided. The third research paper deals with the implementation and application of the model class introduced in the second article. In order to make the class of multivariate ordinal regression models more accessible, the R package mvord and the complementary paper included in this dissertation have been developed. The mvord package is available on the "Comprehensive R Archive Network" (CRAN) for free download and enhances the available ready-to-use statistical software for the analysis of correlated ordinal data. In the creation of the package a strong emphasis has been put on developing a user-friendly and flexible design. The user-friendly design allows end users to estimate in an easy way sophisticated models from the implemented model class. The end users the package appeals to are practitioners and researchers who deal with correlated ordinal data in various areas of application, ranging from credit risk to medicine or psychology.