Interaction and marginal effects are often an important concern, especially when variables are allowed to interact in a nonlinear model. In a linear model, the interaction term, representing the interaction effect, is the impact of a variable on the marginal effect of another variable. In a nonlinear model, however, the marginal effect of the interaction term is different from the interaction effect. This report provides a general derivation of both effects in a nonlinear model and a linear model to clearly illustrate the difference. These differences are then demonstrated with empirical data. The empirical study shows that the corrected interaction effect in an ordered logit or probit model is substantially different from the incorrect interaction effect produced by the margins command in Stata. Based on the correct formulas, this report verifies that the interaction effect is not the same as the marginal effect of the interaction term. Moreover, we must be careful when interpreting the nonlinear models with interaction terms in Stata or any other statistical software package. / text

This study assesses the role of crossing locations and light conditions in pedestrian injury severity through a multivariate regression analysis to control for many other factors that also may influence pedestrian injury severity. Crossing locations include midblock and intersections, and light conditions include daylight, dark with street lighting, and dark without street lighting. The study formulates a theoretical framework on the determinants of pedestrian injury severity, and specifies an empirical model accordingly. An ordered probit model is then applied to the KABCO severity scale of pedestrian injuries which occurred while attempting street crossing in the years 1986 to 2003 in Florida. In terms of crossing locations, the probability of a pedestrian dying when struck by a vehicle, is higher at midblock locations than at intersections for any light condition. In fact, the odds of sustaining a fatal injury is 49 percent lower at intersections than at midblock locations under daylight conditions, 24 percent lower under dark with street lighting conditions, and 5 percent lower under dark without street lighting conditions. Relative to dark conditions without street lighting, daylight reduces the odds of a fatal injury by 75 percent at midblock locations and by 83 percent at intersections, while street lighting reduces the odds by 42 percent at midblock locations and by 54 percent at intersections.

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.

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.

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.

[pt] A modelagem de dados que qualificam as transações de ativos financeiros, tais como, preço, spread de compra e venda, volume e duração, vem despertando o interesse de pesquisadores na área de finanças, levando a um aumento crescente do número de publicações referentes ao tema. As primeiras propostas se limitaram aos modelos de duração. Mais tarde, o impacto da duração sobre a volatilidade instantânea foi analisado. Recentemente, Manganelli (2002) incluiu dados referentes aos volumes transacionados dentro de um modelo vetorial. Neste estudo, nós estendemos o trabalho de Manganelli através da inclusão do spread de compra e venda num modelo vetorial autoregressivo, onde as médias condicionais do spread, volume, duração e volatilidade instantânea são descritas a partir de uma formulação exponencial chamada Exponential Multivariate Autoregressive Conditional Model (EMACM). Nesta nova proposta, não se fazem necessárias a adoção de quaisquer restrições nos parâmetros do modelo, o que facilita o procedimento de estimação por máxima verossimilhança e permite a utilização de testes de Razão de Verossimilhança na especificação da forma funcional do modelo (estrutura de interdependência). Em paralelo, a questão de antecipar movimentos nos preços de ativos financeiros é analisada mediante a utilização de um procedimento integrado, no qual, além da modelagem de dados financeiros de alta freqüência, faz-se uso de um modelo probit ordenado contemporâneo. O EMACM é empregado com o objetivo de capturar a dinâmica associada às variáveis e sua função de previsão é utilizada como proxy para a informação contemporânea necessária ao modelo de previsão de preços proposto. / [en] The availability of high frequency financial transaction data - price, spread, volume and duration -has contributed to the growing number of scientific articles on this topic. The first proposals were limited to pure duration models. Later, the impact of duration over instantaneous volatility was analyzed. More recently, Manganelli (2002) included volume into a vector model. In this document, we extended his work by including the bid-ask spread into the analysis through a vector autoregressive model. The conditional means of spread, volume and duration along with the volatility of returns evolve through transaction events based on an exponential formulation we called Exponential Multivariate Autoregressive Conditional Model (EMACM). In our proposal, there are no constraints on the parameters of the VAR model. This facilitates the maximum likelihood estimation of the model and allows the use of simple likelihood ratio hypothesis tests to specify the model and obtain some clues about the interdependency structure of the variables. In parallel, the problem of stock price forecasting is faced through an integrated approach in which, besides the modeling of high frequency financial data, a contemporary ordered probit model is used. Here, EMACM captures the dynamic that high frequency variables present, and its forecasting function is taken as a proxy to the contemporaneous information necessary to the pricing model.

Doctor of Philosophy / Civil Engineering / Sunanda Dissanayake / Safe and efficient flow of traffic through work zones must be established by improving work zone conditions. Therefore, identifying the factors associated with the severity and the frequency of work zone crashes is important. According to current statistics from the Federal Highway Administration, 2,372 fatalities were associated with motor vehicle traffic crashes in work zones in the United States during the four years from 2010 to 2013. From 2002 to 2014, an average of 1,612 work zone crashes occurred in Kansas each year, making it a serious concern in Kansas. Objectives of this study were to analyze work zone crash characteristics, identify the factors associated with crash severity and frequency, and to identify recommendations to improve work zone safety. Work zone crashes in Kansas from 2010 to 2013 were used to develop crash severity models. Ordered probit regression was used to model the crash severities for daytime, nighttime, multi-vehicle and single-vehicle work zone crashes and for work zones crashes in general. Based on severity models, drivers from 26 to 65 years of age were associated with high crash severities during daytime work zone crashes and driver age was not found significant in nighttime work zone crashes. Use of safety equipment was related to reduced crash severities regardless of the time of the crash. Negative binomial regression was used to model the work zone crash frequency using work zones functioned in Kansas in 2013 and 2014. According to results, increased average daily traffic (AADT) was related to higher number of work zone crashes and work zones in operation at nighttime were related to reduced number of work zone crashes. Findings of this study were used to provide general countermeasure ideas for improving safety of work zones.

The overall goal of this study was to analyse the welfare effect of improved wastewater treatment with the view of making policy recommendations for sustainable urban and peri-urban irrigation agriculture in Kenya. This goal was achieved by investigating three specific objectives. The first objective was to assess the farmers’ awareness of health risks in urban and peri-urban wastewater irrigation. Second objective was to analyse the factors that affect the choice of low-risk adaptations in reuse of untreated wastewater for irrigation. The third objective was to estimate the value that urban and peri-urban farmers who practice wastewater irrigation impute to improvements in specific characteristics of the wastewater input in agriculture. In order to achieve the first objective, an ordered probit model was used to identify the factors that influence farmers’ awareness of health risks in untreated wastewater irrigation. The model was fitted to data collected from a cross-sectional survey of 317 urban farm households in the Kibera informal settlement of Kenya. Results of this study show that gender of household head, household size, education level of household head, farm size, ownership of the farm, membership to farmers’ group, and market access for the fresh produce significantly affect awareness of farmers about health risks in wastewater irrigation. Therefore, there is need for awareness programs to promote public education through regular training and local workshops on wastewater reuse in order to improve the human capital of the urban and peri-urban farmers. To achieve the second objective, the study used a multinomial logit model to analyse the farmers’ choice of low-risk adaptations in untreated wastewater irrigation. A survey of 317 urban and peri-urban farmers was conducted and measures for risk-reduction in wastewater reuse were analysed. The urban and peri-urban farmers were found to have adopted low-risk wastewater irrigation techniques such as cessation of irrigation before harvesting, crop restriction and safer application methods. Results of the study show that adoption of risk-reduction measures is significantly influenced by the following factors: household size, age of the household head, education of household head, access to extension, access to media, access to credit, farmers’ group membership, and risk awareness. Also, marginal analysis of the coefficients confirmed the socio-economic characteristics are key determinants in adoption of low-risk measures in wastewater reuse. The study recommends that policies in support of low-risk urban and peri-urban irrigation agriculture should disaggregate farmers according to their socio-economic and institutional characteristics in order to achieve their intended objectives. To achieve the third objective, the study employed the discrete choice experiment approach to estimate the benefits farmers impute to improvements in attributes of the wastewater irrigation input, whose aim is to reduce the health risks associated with untreated wastewater irrigation. Urban and peri-urban farmers who practice wastewater irrigation drawn from Motoine-Ngong River in Nairobi were randomly selected for the study. A total of 241 farmers completed the presented choice cards for the choice model estimation. A random parameter logit model was used to estimate the individual level willingness to pay for wastewater treatment. The results show that urban and peri-urban farmers are willing to pay significant monthly municipality taxes for treatment of wastewater. Conclusion of this study was that, quality of treated wastewater, quantity of treated wastewater and the riverine ecosystem restoration are significant factors of preference over policy alternative designs in wastewater treatment and reuse. / Thesis (PhD)--University of Pretoria, 2013. / gm2014 / Agricultural Economics, Extension and Rural Development / unrestricted

在金融海嘯之後, 信用風險的重要性益發為銀行金融業所重視。 為深入探索此議題, 本文以 CreditMetrics(TM) 模型為基底, 設定台灣 458 間上市櫃公司為虛擬資產組合, 做出其資產組合價值分配與資產組合損失分配, 以估量信用風險的大小, 提供銀行業計提資本時一個適當的方向。 在模型上, 本文採納 CreditMetrics(TM) 考量交易對手資產報酬率相關性的優點, 此點使我們交易對手評等的移轉產生相關性, 不致低估信用風險; 並修正其以外部評等機構所提供的無條件移轉矩陣為模型參數的設定, 使用排序普羅比模型 (Ordered Probit Model) 在移轉矩陣上引入總體因子, 搭配 Svensson 四因子模型所估計的放款殖利率, 做出條件情境的的經濟資本, 增加資本計提的準確度。 此外, 為了解總體因子的重要性, 本文將之與評等因子做比較。 實證結果發現, 加入總體因子會對信用風險造成一定程度的衝擊, 銀行業實不宜再以無條件情境做為計提資本的標準。 而在評等與曝險額呈現正相關的條件下, 評等因子的重要性比起總體因子有過之而無不及。 銀行業在計提資本時, 與其費盡心思在模型中納入總體因子, 也許應該先看看評等是否已經納入考量。

Contemporary research indicates that the era of autonomous vehicles (AVs) is not only inevitable but may be reached sooner than expected; however, not enough research has been done to address road infrastructure readiness for supporting AV operations. Highway agencies at all levels of governments seek to identify the needed infrastructure changes to facilitate the successful integration of AVs into the existing roadway system. Given multiple sources of uncertainty particularly the market penetration of AVs, agencies find it difficult to justify the substantial investments needed to make these infrastructure changes using traditional value engineering approaches. It is needed to account for these uncertainties by doing a phased retrofitting of road infrastructure to keep up with the AV market penetration. This way, the agency can expand, defer, or scale back the investments at a future time. This dissertation develops a real options analysis (ROA) framework to address these issues while capturing the monetary value of investment timing flexibility. Using key stakeholder feedback, an extensive literature review, and discussions with experts, the needed AV-motivated changes in road infrastructure were identified across two stages of AV operations; the transition phase and the fully-autonomous phase. For a project-level case study of a 66-mile stretch of Indiana’s four-six lane Interstate corridor, two potential scenarios of infrastructure retrofitting were established and evaluated using the net present value (NPV) and ROA approaches. The results show that the NPV approach can lead to decisions at the start of the evaluation period but does not address the uncertainty associated with AV market penetration. In contrast, ROA was found to address uncertainty by incorporating investment timing flexibility and capturing its monetary value. Using the dissertation’s framework, agencies can identify and analyze a wide range of possible scenarios of AV-oriented infrastructure retrofitting to enhance readiness, at both the project and network levels.