Submitted by Vitor Souza (vitor.souza@fgv.br) on 2008-10-17T15:27:55Z No. of bitstreams: 1 055204042-Tatiana_Coimbra.pdf: 292768 bytes, checksum: f1bfd26bd7e333c30122abfb314cef65 (MD5) / Approved for entry into archive by Francisco Terra(francisco.terra@fgv.br) on 2008-10-17T15:59:44Z (GMT) No. of bitstreams: 1 055204042-Tatiana_Coimbra.pdf: 292768 bytes, checksum: f1bfd26bd7e333c30122abfb314cef65 (MD5) / Made available in DSpace on 2008-10-17T15:59:45Z (GMT). No. of bitstreams: 1 055204042-Tatiana_Coimbra.pdf: 292768 bytes, checksum: f1bfd26bd7e333c30122abfb314cef65 (MD5) / A teoria de escolha do portfólio ótimo, desenvolvida a partir da análise da média-variância de Markowitz (1952) deu início ao estudo de vários destes conceitos. A conclusão definitiva deste modelo é que todos os investidores que levam somente em conta a média e o desvio padrão para análise dos investimentos terão o mesmo portfólio de ativos de risco. Investidores conservadores combinarão este portfólio com ativos livres de risco para compor uma carteira menos arriscada. Investidores moderados reduzirão a quantia em ativos livres de risco, aumentando, assim seu risco total. E investidores agressivos podem até contrair empréstimos para obter um portfólio mais arriscado. Os administradores financeiros têm, tradicionalmente, resistido ao simples conselho sobre investimentos embutido nesta teoria. Esta resistência pode, até certo ponto, ser justificada pela necessidade de cada investidor construir seu portfólio refletindo suas preferências e necessidades particulares. Portanto, a gestão da riqueza é um processo direcionado pelas necessidades do indivíduo e não pelos produtos disponíveis. Logo, a análise acadêmica tradicional de escolha do portfólio ótimo precisa ser modificada com o intuito de tratar tais individualidades. O objetivo deste trabalho é usar a base de dados para comparar os resultados empíricos sobre alocação de portfólio à luz da teoria de investimentos com os resultados obtidos através de um questionário respondido pelos funcionários da Souza Cruz, onde utilizaremos um modelo de regressão ordered probit, que prevê a separação em três níveis, dependentes entre si.

An important part when managing credit risk is to assess the probability of default of different counterparties. Increases and decreases in such probabil- ities are central components in the assessment, and this is where transition matrices become useful. These matrices are commonly used tools when as- sessing counterparty credit risk, and contain the probability of default, as well as the probability to migrate between different predefined rating classifica- tions. These rating classifications are used to reflect the risk taken towards different counterparties. Therefore, it is important for financial institutions to develop accurate transition matrix models to manage predicted changes in credit risk exposure. This is because counterparty creditworthiness and prob- ability of default indirectly affect expected loss and the capital requirement of held capital. This thesis will analyze how two specific models perform when used for generating transition matrices. These models will be tested to investigate their performance when predicting rating transitions, including probability of default. / En viktig del vid hanteringen av kreditrisk är att bedöma sannolikheten för fallissemang för olika motparter. Ökningar och minskningar i dessa sanno- likheter är centrala komponenter i bedömningen, och det är här migrations- matriser blir användbara. Dessa matriser är vanligt förekommande verktyg vid bedömning av kreditrisk mot olika motparter och innehåller sannolikheten för fallissemang samt sannolikheten att migrera mellan olika fördefinierade be- tygsklassificeringar. Dessa betygsklassificeringar används för att återspegla den risk som tas mot olika motparter. Det är därför viktigt för finansinstitut att utveckla träffsäkra migrationsmatris modeller för att hantera förväntade förändringar i kreditriskexponering. Detta beror på att kreditvärdigheten hos motparter samt sannolikheten för fallissemang indirekt påverkar expected loss och kapitalkrav. Detta examensarbete kommer att analysera hur två specifika modeller presterar när de används för att generera migrationsmatriser. Dessa mod- eller kommer att testas för att undersöka hur de presterar när de används för att förutsäga övergångar inom betygsklassificering, inklusive sannolikheten för fallissemang.

Correlated ordinal data typically arise from multiple measurements on a collection of subjects. Motivated by an application in credit risk, where multiple credit rating agencies assess the creditworthiness of a firm on an ordinal scale, we consider multivariate ordinal models with a latent variable specification and correlated error terms. Two different link functions are employed, by assuming a multivariate normal and a multivariate logistic distribution for the latent variables underlying the ordinal outcomes. Composite likelihood methods, more specifically the pairwise and tripletwise likelihood approach, are applied for estimating the model parameters. We investigate how sensitive the pairwise likelihood estimates are to the number of subjects and to the presence of observations missing completely at random, and find that these estimates are robust for both link functions and reasonable sample size. The empirical application consists of an analysis of corporate credit ratings from the big three credit rating agencies (Standard & Poor's, Moody's and Fitch). Firm-level and stock price data for publicly traded US companies as well as an incomplete panel of issuer credit ratings are collected and analyzed to illustrate the proposed framework. / Series: Research Report Series / Department of Statistics and Mathematics

Using paired samples t-tests, this study examines attitudes toward government intervention to decrease the consumption of tobacco, alcohol, and sugar to improve public health in Sweden. The effects of the four sociodemographic variables gender, age, education, and income on attitudes toward health policies are tested using Ordinary Least Squares and ordered probit regressions. The research is performed using cross-sectional data which is supplied by a national survey. The results show that tobacco should be regulated the most, followed by alcohol and lastly sugar. According to the respondents, tobacco and alcohol consumption need clear societal restrictions while individuals should be responsible for their sugar consumption. This implies that tobacco and alcohol restrictions introduced by the government should be effective and should, therefore, reduce the consumption and subsequently decrease a country’s economic costs. The opposite is true for sugar policy. Women, younger people, highly educated people, and people with higher incomes are positively related to support toward tobacco restrictions. Women, younger people, and highly educated people show more support for alcohol restrictions. Lastly, respondents with higher levels of education are more supportive of sugar restrictions.

There are several well-known negative side effects associated with pesticide use such as health problems and environmental pollution.  Integrated Pest Management (IPM) seeks to minimize pesticide use while reducing pest infestation to economically tolerable levels.  The introduction of IPM CRSP activities in Ecuador to institutionalize IPM methods focused on priority crops in the country. This study analyzes adoption and the economic impacts of IPM technologies on potato production in the province of Carchi. A model is estimated in which IPM adoption is discrete and ordered and pesticides expenditures are estimated as a function of education, farming experience, wealth, plot size and farmer being sick due to pesticide use for each level of IPM adoption. Results indicate that farmers who were exposed to certain IPM information sources increased adoption of IPM practices on potatoes, but farmers\' education and experience were not important factors in explaining IPM adoption. The calculated economic benefits in terms of aggregate cost savings per production cycle were $823,000. / Master of Science

Many studies have shown that intersections are among the most dangerous locations of a roadway network. Therefore, there is a need to understand the factors that contribute to traffic crashes at such locations. One approach is to model crash occurrences based on configuration, geometric characteristics and traffic. Instead of combining all variables and crash types to create a single statistical model, this analysis created several models that address the different factors that affect crashes, by type of collision as well as injury level, at signalized intersections. The first objective was to determine if there is a difference between important variables for models based on individual crash types or severity levels and aggregated models. The second objective of this research was to investigate the quality and completeness of the crash data and the effect that incomplete data has on the final results. A detailed and thorough data collection effort was necessary for this research to ensure the quality and completeness of this data. Multiple agencies were contacted and databases were crosschecked (i.e. state and local jurisdictions/agencies). Information (including geometry, configuration and traffic characteristics) was collected for a total of 832 intersections and over 33,500 crashes from Brevard, Hillsborough and Seminole Counties and the City of Orlando. Due to the abundance of data collected, a portion was used as a validation set for the tree-based regression. Hierarchical tree-based regression (HTBR) and ordered probit models were used in the analyses. HTBR was used to create models for the expected number of crashes for collision type as well as injury level. Ordered probit models were only used to predict crash severity levels due to the ordinal nature of this dependent variable. Finally, both types of models were used to predict the expected number of crashes. More specifically, tree-based regression was used to consider the difference in the relative importance of each variable between the different types of collisions. First, regressions were only based on crashes available from state agencies to make the results more comparable to other studies. The main finding was that the models created for angle and left turn crashes change the most compared to the model created from the total number of crashes reported on long forms (restricted data usually available at state agencies). This result shows that aggregating the different crash types by only estimating models based on the total number of crashes will not predict the number of expected crashes as accurately as models based on each type of crash separately. Then, complete datasets (full dataset based on crash reports collected from multiple sources) were used to calibrate the models. There was consistently a difference between models based on the restricted and complete datasets. The results in this section show that it is important to include minor crashes (usually reported on short forms and ignored) in the dataset when modeling the number of angle or head-on crashes and less important to include minor crashes when modeling rear-end, right turn or sideswipe crashes. This research presents in detail the significant geometric and traffic characteristics that affect each type of collision. Ordered probit models were used to estimate crash injury severity levels for three different types of models; the first one based on collision type, the second one based on intersection characteristics and the last one based on a significant combination of factors in both models. Both the restricted and complete datasets were used to create the first two model types and the output was compared. It was determined that the models based on the complete dataset were more accurate. However, when compared to the tree-based regression results, the ordered probit model did not predict as well for the restricted dataset based on intersection characteristics. The final ordered probit model showed that crashes involving a pedestrian/bicyclist have the highest probability of a severe injury. For motor vehicle crashes, left turn, angle, head-on and rear-end crashes cause higher injury severity levels. Division (a median) on the minor road, as well as a higher speed limit on the minor road, was found to lower the expected injury level. This research has shed light on several important topics in crash modeling. First of all, this research demonstrated that variables found to be significant in aggregated crash models may not be the same as the significant variables found in models based on specific crash types. Furthermore, variables found to be significant in crash type models typically changed when minor crashes were added to complete the dataset. Thirdly, ordered probit models based on significant crash-type and intersection characteristic variables have greater crash severity prediction power, especially when based on the complete dataset. Lastly, upon comparison between tree-based regression and ordered probit models, it was found that the tree-based regression models better predicted the crash severity levels.

No description available.

Since the federal budget for EFNEP is over $66 million, it is crucial to know the effectiveness of program attributes that may bolster its effectiveness. The aim of this study was to determine how effectiveness of Youth EFNEP programs in Virginia is affected by participant, instructional and curriculum attributes for youth enrolled in Healthy Weight for Healthy Kids (HWHK). An ordered probit model was employed to study how these attributes affect dependent variables: Whole Grain, Fruits, Colored Vegetables, MyPlate, and Breathe Hard behavioral scores. The model assessed the probability of a participant having an improved score or improved behavior. Participant Attributes Results: In general, the chosen variables for participant characteristics consistently reduced the probability of participants having an improved behavior score. Instructional Attributes Results: African Americans Program Assistants were consistently associated with negative marginal effects on positive behavioral scores with the exception of the positive Breathe Hard behavioral score. Other PA attributes were not consistently associated with any behavioral models but were heterogeneous in terms of their marginal effects on the positive behavioral scores. Curriculum Attributes results: The curriculum attributes had more positive marginal effects than negative marginal effects across all the five behavioral scores. Attributes that were consistently associated with having a positive marginal effect on behavioral scores were program duration, smart foods lesson, and lesson duration. Other HWHK lessons were not consistent in increasing or reducing the probability of an improved behavior. / Master of Science

This study documented the psychometric development of the Adaptive Leadership Competency Profile (ALCP). The ALCP was derived from a qualitative database from the National Science Foundation project (NSF 9422368) and the academic body of literature. Test items were operationalized, and subject matter experts validated 11 macro-leadership competencies and 65 items. Rasch rating scale measurement models were applied to answer the following questions: (a) How well do the respective items of the ALCP fit the Rasch rating scale measurement model for the 11 scales of the ACLP? (b) How well do the person's abilities fit the Rasch rating scale measurement model, using the 11 scales of the ALCP? (c) What are the item separation and reliability coefficients for the 11 ALCP scales? (d) What are the person separation and reliability coefficients for the 11 ALCP scales? This study also sought to discern whether the ALCP could predict leader effectiveness as measured by the likelihood ratio index and frequency of correct predictions indices. The WINSTEPS and LIMDEP programs were used to obtain Rasch calibrations and probit estimates, respectively. The ALCP profiles the frequency and intensity of leadership behavior. Composite measures were calculated and used to predict leadership effectiveness. Results from this study validated 10 competencies and 55 items.

The main role of roadway lighting is to produce quick, accurate and comfortable visibility during nighttime conditions. It is commonly known that good lighting levels enable motorists, pedestrians and bicyclists to obtain necessary visual information in an effective and efficient manner. Many previous studies also proved that roadway lighting minimizes the likelihood of crashes by providing better visibility for roadway users. Appropriate and adequate roadway lighting illuminance levels for each roadway classification and pedestrian areas are essential to provide safe and comfortable usage. These levels are usually provided by national, or local standards and guidelines. The Florida Department of Transportation (FDOT) Plan Preparation Manual recommends a roadway lighting illuminance level average standard of 1.0 horizontal foot candle (fc) for all the roadway segments used in this research. The FDOT Plan Preparation Manual also states that this value should be considered standard, but should be increased if necessary to maintain an acceptable uniformity illuminance ratio. This study aimed to find the relationship between nighttime crash injury severity and roadway lighting illuminance. To accomplish this, the research team analyzed crash data and roadway lighting illuminance measured in roadway segments within the West Central Florida Region. An Ordered Probit Model was developed to understand the relationship between roadway lighting illuminance levels and crash injury severity. Additionally, a Negative Binomial Model was used to determine which roadway lighting illuminance levels can be more beneficial in reducing the counts of crashes resulting in injuries. A comprehensive literature review was conducted using longitudinal studies with and without roadway lighting. Results showed that on the same roadways there was a significant decrease in the number of nighttime crashes with the presence of roadway lighting. In this research, roadway lighting illuminance was measured every 40 feet using an Advanced Lighting Measurement System (ALMS) on a total of 245 centerline miles of roadway segments within the West Central Florida Region. The data were mapped and then analyzed using the existing mile post. During the process of crash data analysis, it was observed that rear-end collisions were the most common first harmful event observed in all crashes, regardless of the lighting conditions. Meanwhile, the average injury severity for all crashes, was found to be possible injury regardless of the lighting conditions (day, dark, dusk, and dawn). Finally, this research presented an Ordered Probit Model, developed to understand the existing relationship between roadway lighting illuminance levels and injury severity within the West Central Florida Region. It was observed that having a roadway lighting average moving illuminance range between 0.4 to 0.6 foot candles (fc) was more likely to have a positive effect in reducing the probability of injury severity during a nighttime crash. A Negative Binomial Model was conducted to determine if the roadway lighting average moving illuminance level, found on the Ordered Probit Model was beneficial in reducing crash injury severity during nighttime, would also be beneficial in reducing the counts of crashes resulting in injuries. It was observed that a roadway lighting average moving illuminance, range between 0.4 to 0.6 fc, was more likely to reduce the count of crashes resulting in injuries during nighttime conditions, thus increasing roadway safety. It was also observed that other factors such as pavement condition, site location (intersection or no intersection), number of lanes, and traffic volume can affect the severity and counts of nighttime crashes. The results of this study suggest that simply adding more roadway lighting does not make the roadway safer. The fact is that a reduction in the amount of roadway lighting illuminance can produce savings in energy consumption and help the environment by reducing light pollution. Moreover, these results show that designing roadway lighting systems go beyond the initial design process, it also requires continuous maintenance. Furthermore, regulations for new developments and the introduction of additional lighting sources near roadway facilities (that are not created with the intent of being used for roadway users) need to be created.