This dissertation has four interrelated studies, namely (1) the characterization of milk purchase choices which included the purchase of organic milk, both organic and conventional milk and conventional milk only; (2) the estimation of a single-equation household demand function for organic and conventional milk; (3) the assessment of binary choice models for organic milk using the Brier Probability score and Yates partition, and (4) the estimation of demand systems that addresses the censoring issue through the use of econometric techniques. In the first paper, the study utilized the estimation of both multinomial logit and probit models in examining a set of causal socio-demographic variables in explaining the purchase of three outcome milk choices namely organic milk, organic and conventional milk and conventional milk only. These crucial variables include income, household size, education level and employment of household head, race, ethnicity and region. Using the 2004 Nielsen Homescan Panel, the second study used the Heckman two-step procedure in calculating the own-price, cross-price, and income elasticities by estimating the demand relationships for both organic and conventional milk. Results indicated that organic and conventional milk are substitutes. Also, an asymmetric pattern existed with regard to the substitution patterns of the respective milk types. Likewise, the third study showed that predictive outcomes from binary choice models associated with organic milk can be enhanced with the use of the Brier score method. In this case, specifications omitting important socio-demographic variables reduced the variability of predicted probabilities and therefore limited its sorting ability. The last study estimated both censored Almost Ideal Demand Systems (AIDS) and Quadratic Almost Ideal Demand System (QUAIDS) specifications in modeling nonalcoholic beverages. In this research, five estimation techniques were used which included the usage of Iterated Seemingly Unrelated Regression (ITSUR), two stage methods such as the Heien and Wessells (1990) and the Shonkwiler and Yen (1999) approaches, Generalized Maximum Entropy and the Dong, Gould and Kaiser (2004a) methods. The findings of the study showed that at various censoring techniques, price elasticity estimates were observed to have greater variability in highly censored nonalcoholic beverage items such as tea, coffee and bottled water.

Discrete choice models (DCMs) are used to forecast demand in a variety of engineering, marketing, and policy contexts, and understanding the uncertainty associated with model forecasts is crucial to inform decision-making. This thesis evaluates the suitability of DCMs for forecasting automotive demand. The entire scope of this investigation is too broad to be covered here, but I explore several elements with a focus on three themes: defining how to measure forecast accuracy, comparing model specifications and forecasting methods in terms of prediction accuracy, and comparing the implications of model specifications and forecasting methods on vehicle design. Specifically I address several questions regarding the accuracy and uncertainty of market share predictions resulting from choice of utility function and structural specification, estimation method, and data structure assumptions. I1 compare more than 9,000 models based on those used in peer-reviewed literature and academic and government studies. Firstly, I find that including more model covariates generally improves predictive accuracy, but that the form those covariates take in the utility function is less important. Secondly, better model fit correlates well with better predictive accuracy; however, the models I construct— representative of those in extant literature— exhibit substantial prediction error stemming largely from limited model fit due to unobserved attributes. Lastly, accuracy of predictions in existing markets is neither a necessary nor sufficient condition for use in design. Much of the econometrics literature on vehicle market modeling has presumed that biased coefficients make for bad models. For purely predictive purposes, the drawbacks of potentially mitigating bias using generalized method of moments estimation coupled with instrumental variables outweigh the expected benefits in the experiments conducted in this dissertation. The risk of specifying invalid instruments is high, and my results suggest that the instruments frequently used in the automotive demand literature are likely invalid. Furthermore, biased coefficients are not necessarily bad for maximizing the predictive power of the model. Bias can even aid predictions by implicitly capturing persistent unobserved effects in some circumstances. Including alternative specific constants (ASCs) in DCM utility functions improves model fit but not necessarily forecast accuracy. For frequentist estimated models all tested methods of forecasting ASCs improved share predictions of the whole midsize sedan market over excluding ASC in predictions, but only one method results in improved long term new vehicle, or entrant, forecasts. As seen in a synthetic data study, assuming an incorrect relationship between observed attributes and the ASC for forecasting risks making worse forecasts than would be made by a model that excludes ASCs entirely. Treating the ASCs as model parameters with full distributions of uncertainty via Bayesian estimation is more robust to selection of ASC forecasting method and less reliant on persistent market structures, however it comes at increased computational cost. Additionally, the best long term forecasts are made by the frequentist model that treats ASCs as calibration constants fit to the model post estimation of other parameters.

<p>Blood products are used for transfusion in many routine procedures as well as emergency medical care. The balance between the supply and demand of blood products in Canada is being threatened by an increasing aging population, a growing immigrant population, and advances in medical technology which places additional strain on the blood supply. The objective of this research is to investigate the effects of demographic determinants and clinic accessibility on the frequency of blood donation in Canada excluding the province of Québec, providing a national assessment of blood donor correlates at the individual level. Exploration of these demographic factors in addition to clinic accessibility may help to explain why a limited number of repeat donors are currently contributing, with many donors giving blood only once a year. Repeat donors are vital to maintain a safe and secure blood supply, therefore it is important to retain existing donors in addition to recruiting new volunteers. In this study, individual donor and clinic information is obtained from the Canadian Blood Services 2008 national dataset, with contextual data from the 2006 Canadian Census. Discrete choice models are used to assess the effects of these variables on the frequency of blood donation across the country, highlighting the importance of clinic accessibility. The analysis is prepared for major Census Metropolitan Areas in Canada. Results may contribute to service optimization and targeted advertising, ultimately aiming to encourage the eligible population to donate.</p> / Master of Arts (MA)

The primary objective of this thesis is to assess the different statistical methods used forcalibrating the Samgods model, which is a transportation planning tool employed in Sweden toforecast the demand for freight transport. By focusing on the specific context of national logisticsmodels, this research aims to enhance the accuracy and reliability of the Samgods model throughproposed improvements.In addition to evaluating the calibration techniques for the Samgods model, this thesisexplores the broader application of statistical estimation methods in national logistics models.It examines their potential benefits and limitations in order to shed light on their significance.The findings of this research highlight the crucial role of statistical estimations in improvingthe accuracy of national logistics models, thus enabling better-informed decision-making intransport planning and logistics management.By estimating the cost sensitivity parameter in the Swedish national logistics model, Samgods,this thesis contributes to a deeper understanding of the role of statistical estimations in optimizingsuch models. It underscores the importance of reliable and accurate data analysis in transportationplanning and logistics management. Ultimately, the aim is to provide valuable insights into howstatistical estimations can enhance the effectiveness of national logistics models.

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Esta dissertação visa avaliar o padrão comportamental dos pedestres nos deslocamentos na cidade de Porto Alegre. É abordado a partir da base de dados provenientes da pesquisa de entrevistas domiciliares realizadas em 2003 em Porto Alegre. Para isto, caracterizaram-se os deslocamentos a pé na cidade e identificaram-se as regiões de maior e menor concentração de viagens a pé. A fim de determinar os fatores que influenciam a decisão de caminhar foram estimados modelos logit binomiais para as viagens menores que 2 km em cada uma das regiões identificadas anteriormente. As variáveis explicativas para os modelos analisados incluem características do domicílio (disponibilidade de automóvel e renda por domicílio), dos residentes (idade), das viagens (distância e motivo da viagem), da forma urbana (densidade de domicílios, densidade populacional, comprimento médio das quadras, padrão do sistema viário, tipo de uso do solo e estacionamento tarifado em área pública) e da disponibilidade de transporte coletivo na origem da viagem. Foram consideradas duas categorias de viagens: viagens por motivo trabalho/estudo e viagens por motivo não trabalho/estudo (motivos recreacionais, compras, saúde, assuntos pessoais e outros). Os resultados do estudo mostram que características sócio-econômicas dos residentes, características das viagens e da forma urbana influenciam a escolha do modo a pé. Analisando os valores de elasticidade obtidos para as duas categorias de viagens originadas em Petrópolis e no Centro pode-se inferir que as variáveis que exercem maior influência estão relacionadas principalmente a características da viagem (distância) e à configuração física da rede viária. A análise de sensibilidade evidenciou a sensibilidade do modelo frente a alterações das variáveis estudadas. Os resultados obtidos servem de apoio para um planejamento mais adequado da mobilidade e acessibilidade dos pedestres. / This thesis aims to evaluate the pedestrians’ behavior in Porto Alegre. The study was based on a Porto Alegre household survey of 2003. During the analysis, the pedestrian trips were characterized and the traffic zones with the larger and the smaller number of pedestrian trips were identified. In order to determine the influencing factors related to the walk choice, binomial logit models were developed for trips with less than 2km in each traffic zone previously identified. The explanatory variable used in the models included the characteristics of the household (auto availability and household income), of the household members (age), of the trip (distance and purpose of the trip), of the built environment (housing units density, population density, mean block size, street patterns, land use and public parking), and transit availability in the origin of the trip. Two types of pedestrian travel were considered: work and non-work trips (shopping, health, personal purposes and others). The study results showed that socio-economic characteristics, trip characteristics and local measures of the built environment influence walk modechoice. Elasticity results for the two types of trips, with origin in “Petrópolis” and Downtown, indicate that the most influence variables are connected with trip characteristics (distance) and street design. The sensibility analysis showed the model sensibility strength under the changes introduced in the variables studied. These analysis results may provide support for a better planning for pedestrians’ mobility and accessibility.

This thesis investigates the relationship between conflict, economic growth, state capacity and natural resources in Sub-Saharan Africa. It contributes to the limited research in this area and empirically examines these relationships using different econometric models. The first empirical chapter uses a panel dataset that covers the period 1997 - 2013 to analyse the effects of economic growth on conflict in Nigeria using the negative binomial model. The findings support the direct relationship between economic growth and conflict in Nigeria. Controlling for other factors, the results indicate that increase in growth rate - measured by annual growth rate of GDP per capita - decreases the expected number of conflicts. The study finds no evidence of a relationship between levels of wealth in a state and the incidence of conflicts. The analysis controls for factors such as spill-over effects from other states and year and state effects. Finally, to address potential concerns that economic growth could be a cause of conflict or that other unobserved factors could confound the relationship between economic growth and conflict, the chapter employs instrumental variable (IV) estimation using percentage change in rainfall as an instrument. The results with the IV estimation are similar to the results without IV in terms of both sign and significance, indicating that the negative effect of economic growth on conflicts is not due to reverse causality or omitted variables. For robustness checks, a Panel Autoregressive model (PVAR) is also employed. The second empirical chapter analyses the effect of conflict on state capacity in Sub-Saharan Africa. State capacity is measured in terms of fiscal and legal capacity. It also looks at the effects of internal and external conflicts on state capacity. The chapter adopts the Ordinary least squared (OLS) and the system generalised methods of moments (GMM) estimation methods to analyse the panel data consisting of 49 Sub-Saharan countries over the period 2000 - 2015. The results suggest that conflicts have a negative and significant effect on state capacity. However, when military expenditure is used as a proxy for state capacity it is found that conflict strengthens state capacity. The results are consistent with theoretical argument that internal conflicts polarise societies and make it more difficult for governments to reach a consensus in investing in state capacity, while external conflicts mobilise domestic population against a common enemy thereby helping in state capacity building. Finally, the third empirical chapter examines the effect of natural resources on conflict onset and duration using discrete choice models with a dataset covering the period 1980 -2016. The results on the duration analysis show that natural resources prolong duration of conflicts. However, it is found that not all natural resources prolong duration of conflicts. Oil production does not seem to affect duration, whereas oil reserves and gas production lengthens the duration. The findings from the onset analysis show that both production and reserves of natural resources increase the risk of conflict onset.

Travel to outdoor recreational spaces belongs to a general class of research questions for understanding destination and travel mode choices. In travel demand modelling, discrete choice models (DCMs) have been applied to understand and predict a wide range of choices, such as how people choose among alternative destinations for jobs, homes, shopping, personal services etc. Surprisingly, DCMs have rarely been used to understand and model travel to outdoor recreational spaces. In the current literature for modelling travel to outdoor recreational spaces, the established models are Negative Binomial Regression (NBR) models, such as what was used in the UK NEA studies. However, these NBR models were developed to assess the effects of travel to outdoor recreational spaces at a national level, and they are not intended for assessing choices of individual sites. One reason for this is, as identified by previous studies, is that compared with the DCMs, the NBR models have certain limits on estimating people's choice behaviours. There is, therefore, no existing model that can represent and predict how people choose to travel to outdoor recreational spaces. Given the importance of outdoor recreational activities to urban land use planning and public health, this is a clear gap in the field. The aim of this study is to develop a new travel demand model capable of representing and predicting travel to individual outdoor recreational sites. This is achieved by answering four main research questions: First, how to build the new model for outdoor recreational travel? Secondly, is the estimation accurate enough? Thirdly, to what extent can the new model be transferred to destinations outside the case study area? And, finally, how can city planners and designers use this new method? The new model draws upon ideas from random utility theory that underlies the conventional travel demand models to represent trip generation, trip distribution and mode choice. This research follows the standard modelling procedure: data collection and preliminary analysis, model calibration, model validation and model application. The data are collated from a wide range of sources that, importantly for model transferability, cover all areas in England. The new model has been calibrated for a case study area which spanned 14 selected districts in the North-West region. Validation of the new model is based on estimating the numbers of trips to two outdoor recreational sites (Wigg Island and Wigan Flashes) and to nine English National Parks where data on visitor trips exist. In the final stage of the research, the new model is applied to estimate the changes that would arise from planning and design interventions in existing (Wigg Island and Moore Nature Reserve) and proposed (Arpley Country Park) sites. At the end of this process, it is possible to show that the new model can predict the number of trips to individual destinations and that the model can be transferred to other outdoor recreation sites. Furthermore, the new model presented here is capable of predicting the changes in the volume and catchment of visits to an existing green space after land use planning or urban ecological interventions. This is a completely new theoretical model that is focused on understanding and quantifying the travel choices to outdoor recreation sites, which can inform decision makers by forecasting changes in outdoor recreational travel demand, according to different planning scenarios.

The overall goal of the dissertation is to contribute to the growing literature on multiple discrete-continuous (MDC) choice models. In MDC choice situations, consumers often encounter two inter-related decisions at a choice instance – which alternative(s) to choose for consumption from a set of available alternatives, and the amount to consume of the chosen alternatives. In the recent literature, there is increasing attention on modeling MDC situations based on a rigorous underlying micro-economic utility maximization framework. Among these models, the multiple-discrete continuous extreme value MDCEV model (Bhat, 2005, 2008) provides a number of advantages over other models. The primary objective of this dissertation is to extend the MDCEV framework to accommodate more realistic decision-making processes from a behavioral standpoint. The dissertation has two secondary objectives. The first is to advance the current operationalization and the econometric modeling of MDC choice situations. The second is to contribute to the transportation literature by estimating MDC models that provide new insights on individuals’ travel decision processes. The proposed extensions of the MDCEV model include: (1) To formulate and estimate a latent choice set generation model within the MDCEV framework, (2) To develop a random utility-based model formulation that extends the MDCEV model to include multiple linear constraints, and (3) To extend the MDCEV model to relax the assumption of an additively separable utility function. The methodologies developed in this dissertation allow the specification and estimation of complex MDC choice models, and may be viewed as a major advance with the potential to lead to significant breakthroughs in the way MDC choices are structured and implemented. These methodologies provide a more realistic representation of the choice process. The proposed extensions are applied to different empirical contexts within the transportation field, including participation in and travel mileage allocated to non-work activities during various time periods of the day for workers, participation in recreational activities and time allocation for workers, and household expenditures in disaggregate transportation categories. The results from these exercises clearly underline the importance of relaxing some of the assumptions made, not only in the MDCEV model, but in MDC models in general. / text

American households spend over $30 billion on tourism and take over 177 million long-distance leisure trips each year. These trips, and the subsequent vehicle miles traveled, have a significant impact on the transportation systems at major destinations across the country, especially those destinations that are still improving their transportation systems. Surprisingly, not much is known related to this type of travel. This dissertation expands the current knowledge of tourism travel behavior, in terms of how people make decisions regarding long-distance leisure activities and time use. Specifically, this dissertation develops and comprehensively examines a behavioral framework for household tourism time use and activity patterns. This framework combines (and builds upon) theory and methods from both transportation and tourism research fields such that it can be used to improve tourism demand modeling. This framework takes an interdisciplinary approach to describe how long distance leisure travelers allocate and maximize their time use across various types of activities. It also considers the many levels of tourism time use and activity patterns, including the structuring the broad annual leisure activity and time budget, forming individual tourism trips within the defined budget, and selecting specific activities and timing during each distinct tourism trip. Subsequently, this dissertation will additionally apply the time use and activity participation behavioral framework to four critical tourism research topics to demonstrate how the tourism behavioral framework can effectively be used to provide behavioral insights into some of the most commonly studied critical tourism issues. These application topics include household participation in broad tourism travel activities, travel parties’ tourism destination and travel mode selection, individuals’ loyalty towards daily and tourism activities, and travel parties’ participation in combinations of specific tourism trip activities. These application studies incorporate a variety of data sources, decision makers, study scales, situation-appropriate modeling techniques, and economic/individual/environmental factors to capture all aspects of the decision and travel activity-making process. / text