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In this thesis, we study brand choice problem via the following three perspectives: a company's market share management, introduction of customers with different perspectives, and an analysis of an application domain which is illustrative of these issues. Our contributions following these perspectives include: (1) development of a stochastic differential-jump game (SDJG) model for brand competition in a specific situation wherein market share is modeled by a jump-diffusion process, (2) a robust hierarchical logit/probit model for market heterogeneity, and (3) applications of logit/probit model to the dynamic pricing problem occurring in production-inventory systems with jump events. Our research explores the use of quantitative method of operations research to control the dynamics of market share and provides a precise estimation method to integrate more detail information in discrete brand choice models.

L'importance de l'intérêt accordé à la dimension sanitaire réside tout d'abord dans la détermination des conditions de vie des populations, et constitue ainsi un droit social fondamental. Elle réside également dans la place qu'elle occupe dans le processus de développement en ce qu'elle constitue un préalable indispensable pour rompre le cercle vicieux de la pauvreté. Il nous parut donc important de porter une attention particulière sur les tenants et les aboutissements des investissements sanitaires dans un pays où les taux de malnutrition sont des plus alarmants, le Bangladesh.<br />Les analyse proposées reposent sur deux enquêtes ménages : The Demographic and Health Survey, DHS, 2000 et The Matlab Health and Socioeconomic Survey ,MHSS, 1996, conduites toutes deux au Bangladesh. La seconde enquête a été réalisée au Matlab, une zone rurale du Bangladesh.<br />Notre travail de recherche s'oriente dans trois directions auxquelles correspondent les trois parties de cette thèse. Nous nous sommes interrogés dans une première partie sur ce qui détermine la santé des enfants par l'étude des facteurs explicatifs de la santé des enfants. Même si la littérature empirique correspondante est abondante, il n'existe pas de réel consensus sur les déterminants socioéconomiques du statut nutritionnel des enfants. <br />Ensuite et dans une deuxième partie, nous nous sommes intéressés à l'étude de l'impact de ces conditions nutritionnelles et sanitaires sur la formation du capital éducatif des enfants au Bangladesh : en particulier nous avons cherché à évaluer empiriquement les effets de la santé sur la réussite ou la progression scolaire, sur les retards à l'entrée au système éducatif et l'âge à la sortie et enfin sur le niveau d'étude optimal atteint. <br />L'analyse conduite dans cette partie nous a permis de perfectionner les méthodes d'estimations usuelles en redressant les principaux biais récurrents dans la littérature empirique à savoir le biais d'endogénéité de la santé et le biais de sélectivité des données. En le faisant, les résultats font apparaître, que le statut nutritionnel des enfants, exprimé par la taille pour âge, joue un rôle de premier plan dans la hausse de la participation scolaire et la baisse du retard de scolarisation étant donné l'âge. L'influence de la malnutrition est robuste même si l'on contrôle pour les caractéristiques familiales non observées par des procédures d'estimation à effets fixes. Nous estimons qu'une augmentation d'un écart type dans l'indice taille pour âge réduirait le retard potentiel de scolarisation de 0.343 années et qu'une amélioration d'un écart type dans la mesure de malnutrition chronique (taille pour âge) entraînerait un retard à l'entrée d'approximativement deux années. Par ailleurs, une durée moyenne de trois années de retard contribuerait à réduire la richesse totale de l'individu d'environ 23%.<br />Parce qu'un état de santé défavorable peut ne pas constituer le seul frein à l'investissement des parents dans la scolarisation de leurs enfants, nous accordons une attention particulière à l'étude des contraintes de l'offre d'éducation. En effet, les coûts de scolarisations directs et indirects représentent des arguments de dissuasion non négligeables. Nous mettons en œuvre les procédures économétriques adéquates pour estimer les déterminants du niveau optimal d'étude atteint et la probabilité d'avoir déjà été scolarisé, et qui contrôlent simultanément les problèmes d'endogénéité, de censure à droite des données et la nature discrète de la variable dépendante. D'une façon très synthétique, nous montrons que les parents prennent en compte l'ensemble des coûts de scolarisation (croissants) tout au long des cycles scolaires pour décider de l'investissement actuel dans l'éducation primaire de leurs enfants. Cette affirmation nous conduit à envisager une réallocation des dépenses publiques d'éducation entre les secteurs primaires et secondaires afin de réduire l'abandon scolaire précoce.

Income inequality has been on the rise in many industrialised countries since around the 1980’s. In Sweden the increase of income inequality has been particularly large. This in spite of Sweden’s extensive redistribution system and public policy that prioritize equality among its population. This paper investigates a potential factor for the rise in inequality that is yet fairly unexplored, namely immigration. As inequality has increased in Sweden, so has also immigration. Sweden experienced large refugee inflows after the 1970’s, the largest flow consisting of circa 100 000 Yugoslavs during the Bosnian war. This study provides indications on what way immigration shapes the income distribution and lays the ground for prospective studies. Results show that the inflow of new migrants during the early 1990’s in Sweden raises income inequality and it is almost entirely due to increased dispersion in the lower tail of the income distribution.

In this paper I propose Bayesian estimation of a nonlinear panel data model with a fractional dependent variable (bounded between 0 and 1). Specifically, I estimate a panel data fractional probit model which takes into account the bounded nature of the fractional response variable. I outline estimation under the assumption of strict exogeneity as well as when allowing for potential endogeneity. Furthermore, I illustrate how transitioning from the strictly exogenous case to the case of endogeneity only requires slight adjustments. For comparative purposes I also estimate linear specifications of these models and show how quantities of interest such as marginal effects can be calculated and compared across models. Using data from the state of Florida, I examine the relationship between school spending and student achievement, and find that increased spending has a positive and statistically significant effect on student achievement. Furthermore, this effect is roughly 50% larger in the model which allows for endogenous spending. Specifically, a $1,000 increase in per-pupil spending is associated with an increase in standardized test pass rates ranging from 6.2-10.1%.

Multivariate ordinal response data, such as severity of pain, degree of disability, and satisfaction with a healthcare provider, are prevalent in many areas of research including public health, biomedical, and social science research. Ignoring the multivariate features of the response variables, that is, by not taking the correlation between the errors across models into account, may lead to substantially biased estimates and inference. In addition, such multivariate ordinal outcomes frequently exhibit a high percentage of zeros (zero inflation) at the lower end of the ordinal scales, as compared to what is expected under a multivariate ordinal distribution. Thus, zero inflation coupled with the multivariate structure make it difficult to analyze such data and properly interpret the results. Methods that have been developed to address the zero-inflated data are limited to univariate-logit or univariate-probit model, and extension to bivariate (or multivariate) probit models has been very limited to date. In this research, a latent variable approach was used to develop a Mixture Bivariate Zero-Inflated Ordered Probit (MBZIOP) model. A Bayesian MCMC technique was used for parameter estimation. A simulation study was then conducted to compare the performances of the estimators of the proposed model with two existing models. The simulation study suggested that for data with at least a moderate proportion of zeros in bivariate responses, the proposed model performed better than the comparison models both in terms of lower bias and greater accuracy (RMSE). Finally, the proposed method was illustrated with a publicly-available drug-abuse dataset to identify highly probable predictors of: (i) being a user/nonuser of marijuana, cocaine, or both; and (ii), conditional on user status, the level of consumption of these drugs. The results from the analysis suggested that older individuals, smokers, and people with a prior criminal background have a higher risk of being a marijuana only user, or being the user of both drugs. However, cocaine only users were predicted on the basis of being younger and having been engaged in the criminal-justice system. Given that an individual is a user of marijuana only, or user of both drugs, age appears to have an inverse effect on the latent level of consumption of marijuana as well as cocaine. Similarly, given that a respondent is a user of cocaine only, all covariates--age, involvement in criminal activities, and being of black race--are strong predictors of the level of cocaine consumption. The finding of older age being associated with higher drug consumption may represent a survival bias whereby previous younger users with high consumption may have been at elevated risk of premature mortality. Finally, the analysis indicated that blacks are likely to use less marijuana, but have a higher latent level of cocaine given that they are user of both drugs.

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.

Thèse diffusée initialement dans le cadre d'un projet pilote des Presses de l'Université de Montréal/Centre d'édition numérique UdeM (1997-2008) avec l'autorisation de l'auteur.

Obesity, the second leading cause of preventable death in the U.S., and related health problems increase people’s concerns about healthy food consumption. The increased prevalence of obesity is a major concern of societies both in developed and developing countries. Nutrition label usage has been increasing due to the link between diet and health. This study intends to provide a framework for describing profiles of consumers who are more likely to use nutrition labels in USA and Turkey, a developing country with increasing obesity rates in recent years. Empirical results present similarities and differences between consumers’ attributes for food label usage in two countries. The main contribution of this study is to investigate the relationship between the importance of serving size, while the number of expanded portion sized products in the market is increasing, and rising obesity rates. Ordered probit model analysis is used to identify the effects of demographics, health status and other components of the nutrition facts panel on selected dependent variables. Better understanding consumers’ responses to nutrition labels may guide consumers and manufacturers to broaden the communication channels through nutrition labels. The findings of this study can provide useful information to policy makers, agribusinesses, manufacturers and marketing professionals.