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.

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.

In the past few decades spatial econometric models have become a standard tool in empirical research. Nevertheless applications in binary-choice models remain scarce. This paper makes use of Bayesian Spatial Probit Models to model and estimate spatial interactions in location decisions. For this purpose, we focus on the Austrian retail gasoline market, which is going through a process of remarkable structural changes. A short analysis shows that, during the last decade 10.9% of the stations had left the market and a percentage of 29.6% had either left the market or had changed the brand. This paper aims at investigating this process. A special characteristic of this market is the local competition structure which is characterized by spatial dependencies along local competitors. To capture these spatial dependencies and since the dependent variable is binary in nature (an exit had taken place or not), we apply a Bayesian spatial probit model using MCMC estimation on station level data for the whole Austrian retail gasoline market. Our results suggest, that the decision to leave the market, does not only depend on own characteristics, but also on competitors. In particular, we find the exit decisions to exhibit a negative spatial correlation. Moreover, our model allows to quantify spatial spillover effects of this market. (authors' abstract) / Series: Department of Economics Working Paper Series

追求長期穩定的經濟成長是每個國家欲追求的目標，在經濟發展過程中，外在衝擊常常導致經濟體系的景氣循環波動，而短期間景氣循環的大幅波動將不利於經濟體系穩定發展，因為民眾的消費、廠商的投資決策以及政府政策的規劃與實施，都深深受到景氣變動的影響。因此準確預測景氣動向，深受經濟學者、政府以及一般民眾的重視。 預估景氣循環擴張和衰退持續期間的長短並不容易，由美國國家經濟研究局 提供的資料得知，第二次世界大戰後，美國景氣擴張最長的時間，曾經延續了一百零六個月，而最短的則只有十二個月；在景氣衰退方面，最短是六個月，最長則為十六個月。而二次大戰前，時間變化的幅度就更大了。由於景氣變化前的徵兆並不是很顯著，因此許多經濟學者從各種方面來探討與分析景氣循環。 本篇論文引用 ordered Probit 模型對 Dueker (2005) 文章作一個擴展與應用，將Dueker文中原本的二分類：景氣衰退、景氣擴張延伸為景氣三分類：景氣衰退、景氣狀態不明與景氣擴張，帶入 Qual VAR 模型並利用Gibbs sampling模擬未知參數與變數，藉由統計分析，希望能對景氣循環提出一個更為詳細的詮釋。而本篇論文的目的希望在相對於二分類模型，在總體現象上能提供一個更為完善與更明確的描述，使得在分析上能更完整。參考 NBER 所公佈的景氣轉折點並輔以其他指標，將景氣區分為三分類，以 Qual VAR 模型模擬出景氣三分類的景氣指標，再對這個指標做預測分析，並比較美國景氣在二分類與三分類之下的異同。結果指出三分類模型成功的預測出 2002 年第一季到 2003 年第三季美國景氣擴張的狀態，而三選擇模型的模型比起二選擇模型，對於預測景氣狀態有更為明確的判斷，且加入一分類指標，提供新的景氣變動解釋，幫助人們做出更為合適的決策。

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.

This study is based on first-league football teams from England, Spain and Russia. It demonstrates that football tourism has a big potential because the attendance on football stadiums has a positive effect over the number of visitors in the city, and this influence is stronger among teams that are historically more popular. Football trips can also contribute to decreasing the seasonality and the centralization of tourism. The questionnaire designed for supporters demonstrates that a higher percentage of fans of currently less competitive teams tend to visit away games, but it is explained through the fact that more successful clubs have supporters located far away, for whom performing such a thing is more troublesome. English supporters spend less money in their football visiting trips, but their journeys are also shorter and without many activities, which makes their habits different from the ones of a typical tourist. At the same time, Spanish football tourists spend more time and money while travelling. The respondents from both countries face similar problems while visiting a match, and the majority of them are infrastructural. Russian football tourists spend the most time and money in their journeys, visiting many places except for the football match itself, which makes their behaviour the closest to a typical tourist. However, during matches they also suffer from more serious issues related to the unfriendliness of police and of hosts.

Background – This study is an attempt to explore the relationship between microcredit and the socio-economic empowerment of women in rural Bangladesh. Microcredit is simply the extension of a small amount of collateral-free institutional loans to jointly liable poor group members to generate employment and income enhancing activities. As it is too difficult for poor members to get loan from the formal credit institutions, Grameen Bank (GB) or other Non-Government Organizations (NGOs) provide small loans to vulnerable groups of the society by which they are expected to empower over his counterparts. Research questions – RQ1: How does micro-credit affect different indicators of women empowerment in the rural areas of Bangladesh? RQ2– Is the impact different from the male counterparts in the sample households? Purpose – This study is an effort to find the impact of microcredit on a number of indicators of women’s empowerment in the rural areas in Bangladesh. Methodology – Quantitative Regression Techniques such as Ordinary Least Square (OLS) and Instrumental Variable (IV) method have been applied to get the relationship between microcredit and women empowerment. Conclusion – Applying nationally representative cross-section survey data, Bangladesh Integrated Household Survey (BIHS) 2015, this thesis is intended to find the causal linkage between microcredit and women empowerment’s with different dimensions of women’s decisions are taken as empowerment indicators: production, resources, income, leadership, savings and time. The analysis has been conducted at the household level. The study assumes that women empowerment is endogenous. After controlling for endogeneity in the estimation by using an instrumental variable (IV) ‘distance to the market’ this study finds a significant relationship between microcredit and different dimensions of women’s empowerment. Participation in the microcredit program is found to be significant in explaining some of the outcome indicators of empowerment for the sampled households.

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 situation that China's pensions cannot cover the expenditure is becoming more and more serious. On the one hand, it is to increase the rate of return of enterprise annuities. On the other hand, it is to increase the participation rate of enterprise annuities. In order to improve the participation rate of enterprise annuity, it is of great significance to solve the problem of insufficient funds of enterprise annuity from the aspect of supply. Based on this, this dissertation conducts detailed research, focusing on the impact of relevant factors on the participation rate of enterprise annuity from the enterprise level and the individual level. After making a detailed overview of the situation of domestic enterprise annuity, this dissertation analyzes the influencing factors of the participation rate of enterprise annuity of China based on the latest nationally representative micro data CHIP 2018. The analysis uses the "bivariate Probit model", that is, the enterprise level and the individual level, employing 0-1 bivariate as the dependent variables and employee income, employee education, employee gender, enterprise income as independent variables. The analysis also includes other control variables. The empirical evidence found that employee income, employee education, and employee gender have impacts on employee participation in enterprise annuity; enterprise ownership, company income, and the industry in which the company is located have impacts on whether the enterprise establishes an enterprise annuity plan. In addition, there are differences in the state-owned system and private system and industry differences on the establishment of enterprise annuity plans. Finally, this dissertation gives relevant policy suggestions that may provide some theoretical guidance for increasing the participation rate of enterprise annuity.Key words: Enterprise annuity; Participation rate; Bivariate Probit model; Policy suggestions. / Business Administration/Interdisciplinary