一般而言， 銀行決定貸放與否，除了取決於客戶所提供之擔保品、保證 人之財力大小外， 徵信工作人員憑著自己過去經驗主觀之判定亦是一重 要之影響因素。 然而人為所牽涉之因素過於複雜且多變，若稍不謹慎極 可能犯下極大的錯失。 也因此銀行必須發展一套客觀之信用評估模型來 分析客戶的信用，以求降低放款的風險。而一般國內之銀行信用評估模型 及理論大多以混和行業別為研究對象，且多以財務比率分析為主。 但不 同的產業其財務特性有所差異，因此本研究便試圖以塑膠業建立單一產業 之信用評估模型， 以尋求建立更適合之信用評估模型。本研究首先根據 國內外文獻中整理出可供研究之若干財務變數， 再參酌目前銀行所使用 作為徵信評估之財務變數， 由於本模式是採本國塑膠業之資料， 且應用 對象亦主要針對本國銀行，故決定採用目前省屬行庫做徵信評估時所採行 的 17 個財務變數做分析。而後再以塑膠加工業為研究對象， 蒐集民國 79 年至 81 年三十家違約公司與五十七家履約公司共八十七家公司的財 務報表資料以計算其財務比率， 接著便計算此 17 個財務變數是否符合 常態分配之假設，以決定後續所採行之統計方法應為何。 另外根據因素 分析與不採用因素分析分別進行變數之選擇及相關性之分析，以尋找具有 解釋能力之財務變數。 最後以上述所得到之財務變數做自變數， 並將樣 本區分為原始樣本、預測樣本以及全體樣本兩群分別建立 Logit 迴歸模 型，以得到不同分析方法之總正確率、型一誤差、型二誤差及加權效率性 。 爾後再根據結果做較佳模式之選擇與評估，並加以解釋。根據實證結 果顯示， 依不同樣本及不同方法所建立的四個放款信用評估模型中， 經 比較其總正確率及加權效率性的高低後，採用因素分析的模型將優於不採 用因素分析的模型， 而全體樣本所建立的模型會優於原始樣本所建立的 模型。 而由此所得到最佳的信用評估模型為以全體樣本採用因素分析所 得的模型。另外， 本研究是以塑膠業單一產業為研究對象來建立信用評 估模型，與過去採混和產業別的分析有所不同。 而與過去的分析相較後 ，得知其正確區別效果確實較混和產業別所建立的模型為佳。

傳統的犯罪學在於研究犯罪者生理與心理的特性以及外在環境因素，藉以 尋求預防犯罪的策略。其實，預防犯罪的目的是要減少被害者數目。因此 ，吾人希望藉由探討被害者家庭形態的特質與社會犯罪的關係，使防範被 害的政策能夠落實到家庭層面。所以本研究的目的就是要利用統計方法， 透過數字及圖形上的表達，來描述住戶之家計負責人的特性(如性別、年 齡、教育程度)與住戶遭受侵害的事實(曾或否)及類型(如財物侵害、暴力 侵害、其他侵害)之間的關係，期為犯罪統計工作提供一些分析的工具。 在本篇論文，首先利用對數線性模式分析，選出最適合的模式，以解釋家 計負責人之性別、年齡、教育程度與住戶是否遭受侵害等四個變數之間的 關聯性。接著利用推理值模式分析探討家計負責人之性別，年齡，教育程 度這三個解釋變數各分級對住戶遭受侵害之影響的主效應及交互作用效應 。最後則是利用對應分析處理資料，透過對應分析的顯示圖形，可以用來 解釋家計負責人之性別、年齡、教育程度以及住戶遭受侵害的犯罪類型之 間的對應關係。

2007年8月美國爆發次貸危機(Subprime Crisis)，如此新型態的金融危機是否可由金融危機預警系統預測？是本文所欲探討的目標。本文採用訊號方法、固定效果下的Panel Logit Model和CART(Classification and Regression Tree)三種計量方法建構危機預警模型。最後利用美國2006年至2008年資料，驗證本文所建構之預警模型是否能夠有效預測次貸危機的發生。 / “Could banking early warning systems help to predict Sub-prime crisis?” That is the main issue that we want to discuss. We combine three kinds of early warning systems models – Signal Approach, fixed effect panel logit model, and CART approach – to create a new banking early warning system(EWS). We will use the US 2006-2008 data to examine whether this new EWS could predict the Sub-prime crisis correctly.

股利政策之選擇攸關公司資金成本效益與投資者權益，投資者依據公司未來股利發生時點、金額與不確定性等因素來判斷公司的內涵價值（intrinsic value），亦即股利的配發是公司管理當局與股東利益間的抵換。本論文將考量我國企業實務上股利支付內容，將股利形式細分為無股利分配及有股利配發，包含單一股利政策－現金股利、盈餘配股、公積配股，複合股利政策－現金股利搭配盈餘配股、現金股利搭配公積配股、盈餘配股搭配公積配股及綜合股利政策（現金股利、盈餘配股、公積配股三者兼發）之發放模式，共計有八種股利支付類型。 　　共同基金乃證券市場中連接有價證券與投資者間之專業投資管道，由於基金收益直接進行再投資產生之租稅規避濾網（tax-free filter）效果，藉以探討公司選擇股利政策時之顧客效應。同時將影響股利政策制定之構面因素－公司的股利配發來源、獲利性狀況、資金融通與債務償還能力、企業未來投資成長機會、產業類別與租稅改革制度等一併納入分析檢證。 　　本論文採用實證模型分析為主，文獻檢閱分析為輔，以1995- 2000年我國上市公司曾被國內股票型投信基金選為投資標的者，運用Mutinomial logit model及股利政策相關文獻，建構本研究實證模型；在政策意涵上，股利稅制的規劃與共同基金租稅課徵問題亦一併討論，以檢討我國現行稅制環境是否健全。 　　本研究所獲結論歸納如下： 　　(1) 共同基金持股比重變動對股利政策影響存在明顯差異 　　　　股票型基金持股比重大小對現金股利搭配盈餘配股與盈餘配股搭配公積配股選擇皆有顯著正效果，但是否基於股東特性而論則難以斷定。現行我國共同基金持股率占公司股權結構比重不大、透過除權日規避股利發放及大股東利用共同基金進行投信交叉持股等問題，皆是造成實證結果與預期相左之可能原因。 　　(2) 租稅制度改革對股利配發內容的選擇扮演要角 　　　　兩稅合一制度實施後，總股利支付率較實施前為高，主要在於規避保留盈餘加徵10﹪的稅負及股利的發放具有抵稅權，使公司不再傾向保留盈餘而提高發放股利之意願。 　　(3) 股利配發形式選擇 　　　　◎支持股利配發來源與股利政策的關係 　　　　◎公司舉債程度高低與現金股利配發機率呈負相關 　　　　◎公司獲利性狀況攸關股利發放與否 　　　　◎未來成長機會與現金股利配發呈反向關係 　　　　◎股利政策具有產業差異性，高科技產業傾向選擇股票股利偏 高之股利組合政策 　　以公司立場而言，宜需定期評估經營利基與股利政策選擇因素，建立一套合理之股利預測分析模式，並考量平衡股利政策（現金股利與股票股利搭配）模式，以減緩我國企業普遍採股票股利配發造成股權過度膨脹及盈餘稀釋之弊。從投資者角度來看，共同基金係專業化投資管道，藉由大眾資金匯集之規模效益，能使資金有效配置（或風險分散）並提升國內專業法人比重，減緩市場投機風氣。然共同基金投資相關法令之設立仍需更臻完備，稅制環境對公司股利決策具重要影響性，對追求租稅中立目標而言，證券交易所得是否復徵所得稅，亦是政府單位需審慎考量的議題。

景氣循環的擴張、收縮期的認定，以及轉折點的預測，向來是經濟學家、政府機構、企業及一般大眾所關注的焦點。越能掌握經濟發展的狀況，而適切的提早做出反應，越能充分利用經濟情勢的不同，以達成獲利的目標，或是減少企業的虧損。 本文即嘗試以票券、債券、股票、外匯及金融機構等金融變數，透過Kaminsky, Lizondo and Reinhart (1998)用以建構銀行危機預警綜合指標之訊號法（Signal Extraction Approach），以及可以解釋logit模型係數之發生比率（odds ratio），分別結合二元 logit 模型，建構預測景氣循環轉折點之金融綜合指標。 研究結果顯示，於1990年1月至2009年6月期間，金融指標對景氣循環轉折點的確有預測能力，且不論是預測景氣谷底或是高峰，表現最優良之綜合指標正確率皆高達九成以上，且不論是預測景氣之高峰或谷底，以發生比率建構之二元logit模型都有最好的預測能力。 / To confirm the expansion and contraction of business cycle, and to forecast the turning point, has always been economists, government institutions, enterprises and the public cared about. The more control on the state of economic development and take advantage of the different economic situation, the more they could response appropriately, and furthermore this could maximize their profit or reduce business losses. This article attempts to use financial variables, such as the indicators about bills, bonds, equities, foreign exchange, financial institutions, and use signal approach which was created by Kaminsky, Lizondo and Reinhart in 1998 to construct a composite index warning the banking crisis early. I also try to use odds ratio combine with binary logit model in order to predict business cycle turning point. The results show that in January 1990 to June 2009, the financial indicators of business cycle turning points does have predictive power, and the accurate prediction of the index over ninety percent. The result also show that the best performance of the model is combination of odds ratio and binary logit method.

Travel demand forecasting is essential for many decisions, such as infrastructure investments and policy measures. Traditionally travel demand modelling has considered trip frequency, mode, destination and route choice. This thesis considers two other choice dimensions, hypothesised to have implications for travel demand forecasting. The first part investigates how the increased possibilities to overcome space that ICT (information and communication technology) provides, can be integrated in travel demand forecasting models. We find that possibilities of modelling substitution effects are limited, irrespective of data source and modelling approach. Telecommuting explains, however, a very small part of variation in work trip frequency. It is therefore not urgent to include effects from telecommuting in travel demand forecasting. The results indicate that telecommuting is a privilege for certain groups of employees, and we therefore expect that negative attitudes from management, job suitability and lack of equipment are important obstacles. We find also that company benefits can be obtained from telecommuting. No evidences that telecommuting gives rise to urban sprawl is, however, found. Hence, there is ground for promoting telecommuting from a societal, individual and company perspective. The second part develops a departure time choice model in a mixed logit framework. This model explains how travellers trade-off travel time, travel time variability, monetary and scheduling costs, when choosing departure time. We explicitly account for correlation in unobserved heterogeneity over repeated SP choices, which was fundamental for accurate estimation of the substitution pattern. Temporal constraints at destination are found to mainly restrict late arrival. Constraints at origin mainly restrict early departure. Sensitivity to travel time uncertainty depends on trip type and intended arrival time. Given appropriate input data and a calibrated dynamic assignment model, the model can be applied to forecast peak-spreading effects in congested networks. Combined stated preference (SP) and revealed preference (RP) data is used, which has provided an opportunity to compare observed and stated behaviour. Such analysis has previously not been carried out and indicates that there are systematic differences in RP and SP data. / QC 20100825

The National Transportation System (NTS) is undoubtedly a complex system-of-systems---a collection of diverse 'things' that evolve over time, organized at multiple levels, to achieve a range of possibly conflicting objectives, and never quite behaving as planned. The purpose of this research is to develop a virtual transportation architecture for the ultimate goal of formulating an integrated decision-making framework. The foundational endeavor begins with creating an abstraction of the NTS with the belief that a holistic frame of reference is required to properly study such a multi-disciplinary, trans-domain system. The culmination of the effort produces the Transportation Architecture Field (TAF) as a mental model of the NTS, in which the relationships between four basic entity groups are identified and articulated. This entity-centric abstraction framework underpins the construction of a virtual NTS couched in the form of an agent-based model. The transportation consumers and the service providers are identified as adaptive agents that apply a set of preprogrammed behavioral rules to achieve their respective goals. The transportation infrastructure and multitude of exogenous entities (disruptors and drivers) in the whole system can also be represented without resorting to an extremely complicated structure. The outcome is a flexible, scalable, computational model that allows for examination of numerous scenarios which involve the cascade of interrelated effects of aviation technology, infrastructure, and socioeconomic changes throughout the entire system.

Southeastern states have about 26 percent of the nations total fatalities, and are about 24 percent above the national mean over recent years. Descriptive statistics, graphs, and figures are used to illustrate and quantify the crash trends, which depict a comprehensive picture of status and trends of the fatal crashes in southeastern states. The severity of crashes is studied as a function of characteristics of the person involved in the crash, vehicle, traffic condition, physical road geometry, and environmental factors. Detailed geometric feature data were collected for this study, which makes it possible to investigate the relationship between geometric features and crash severity. This study identifies causal factors contributing to the high fatality rate in southeastern states, and sheds light on the differences and similarities among these states for reducing the severity of fatal crashes, by developing multinomial logit models to explain the severity and type of fatal crashes.

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