本研究採用門檻迴歸模型 (Threshold Autoregression Model)，試圖找出董監事等內部人之申報轉讓比率、持股比率及質押比率等門檻值，進而分析門檻值以內及以外，指標對於代理變數：融資成長率、營收成長率以及本益比與加權指數報酬率的影響程度與方向。本研究實證結果發現： 一、在申報轉讓比率方面： 當申報轉讓比率低於門檻值，存在所謂的群聚效果。當申報轉讓比率高於門檻值時，市場動能與加權指數報酬率無顯著關係，投資人於此階段進行投資決策時應該要謹慎小心。 二、在持股比率方面： 在持股比率低於門檻值時，加權指數報酬率對於前期營收成長率表現的修正幅度較大，意謂著董監事等內部人根據其對未來營收資訊掌握的優勢，反應其對營收資訊的真實性，而藉由持股轉讓的行為，使加權指數大幅度的修正。 三、在質押比率方面： 不管高於或低於門檻值，均無法利用董監事等內部人質押比率為門檻變數來分析本益比效果對加權指數報酬率的影響。造成其檢定失效的原因，可能是樣本小且模型受到極端值的影響所造成。

生育率的降低是影響台灣地區近年來人口老化的顯著因素，因其變化幅度通常高於死亡率，對人口結構的影響較大。過去台灣地區生育率研究多為整體生育(如：總生育率、年齡別生育率)趨勢的模型，較少探討台灣各地區的特色。為能更深入瞭解台灣生育行為變化的特性，本文將生育率的研究層面由整體的資料，延伸至全台灣地區的各鄉鎮市區（不含離島地區有350個鄉鎮市區），希冀能更精確地找出與台灣地區生育率持續下降的相關因素。本文分為兩個部份，以鄉鎮市區的年齡別婦女生育率與年齡別有偶婦女生育率為研究對象，資料時間為1991、1992、2001、2002年：第一部份探討各鄉鎮市區的生育率數值間是否存在空間相關性，並進一步瞭解生育率較高（或較低）的地區是否有聚集的現象。第二部份則套用空間迴歸模型探討與生育率數值有關的因素（例如：人口密度、教育程度等），更精確且客觀地提供生育率未來趨勢的建議。 關鍵字：生育率、人口老化、空間統計、空間群聚、空間迴歸 / Both the fertility rates and mortality rates, especially the fertility rates, have been experiencing dramatic decreases in recent years, and the population aging thus has become one of the major concerns in Taiwan area. In order to identify the factors that are related to the decrease of fertility rates, unlike the previous works that deal with the aggregate national data, we will study the fertility pattern in township level. We will use the data of age-specific fertility rates and total fertility rates in 1991, 1992, 2001, and 2002 in 350 townships of Taiwan area. This study will be separated into two parts. First, we shall explore if there is spatial correlation among 350 townships of Taiwan area and detect if there are spatial clusters for higher fertility townships. The second part of this project will be focused on the spatial regression model. We will use this model to determine the factors that are highly correlated to the dropping of fertility rates. Key Words: Fertility Rates, Aging Population, Spatial Statistics, Spatial Clustering, Spatial Regression

1999年新巴塞爾協定規定鼓勵銀行採用內部信用評等法(internal ratings based approach)，以衡量貸款者無法償還之風險以計提最低資本。為因應此一授信風險控管之需要，銀行亟需建立一套有效之財務危機預警系統，以判定銀行授信客戶發生財務危機之機率。 本研究運用羅吉斯迴歸分析(logistic regression analysis)與離散時間涉險分析(discrete-time survival analysis)分法於三種相互具有巢狀式關連性之財務危機預測模型，逐步加入財務、非財務及公司治理變數，以便在同一種分析方法下比較三種模型，以及在同一種模型下比較兩種分析方法。實證結果顯示，就樣本期間內而言，同一種分析方法下模型之財務危機預測能力，隨著不同種類解釋變數之加入而逐步提高。然而，就樣本期間外而言，同一種分析方法下模型之財務危機預測能力，並未隨著不同種類解釋變數之加入而逐步提高，但分類能力皆十分優良；而在同一種模型下離散時間涉險分析方法之整體分類能力皆高於羅吉斯迴歸分析方法。 / The 1999 Basel II Accord suggests banks measure the impossibility of reimbursement of debtors to calculate capital minimums by internal ratings-based approach. To reduce the credit risk, it is important that banks construct accurate financial distress prediction systems to determine the probability of financial distress of debtors. This study employs logistic regression and discrete-time hazard analysis to construct nested models to which the financial, non-financial, and corporate governance corporate variables are added step by step. I therefore make comparison of the performance of three models under logistic regression and discrete-time hazard analysis, respectively. Meanwhile, the comparison of the performance of logistic regression and discrete-time hazard analyses under each of three models is also made. The empirical results show that the in-sample predictive ability of financial distress is enhanced by gradually incorporating different kinds of variables in both analyses. Although the out-of-the-sample predictive ability of financial distress is not improved by gradually incorporating different kinds of variables in one analysis, the model performance is quite well overall. The entire discriminability of discrete-time hazard analysis is better than logistic regression under each model.

本研究主要探討台灣上市櫃公司從事首次現金增資之決策受何種公司特徵所影響，並進一步探討進行其首次現金增資的宣告效果影響因素。 本研究針對1981年至2010年共30年期間於台灣上市上櫃之公司其首次現金增資之情形做為探討對象，採用Cox-proportional Hazard Regression檢定影響上市櫃公司進行首次現金增資時程之公司特徵。實證結果顯示，營收成長率越高、規模越大且獲利能力較差的公司會傾向越快進行首次現金增資。同時也針對上市櫃年度其市場情形加以探討，發現於市場處於熱市時上市櫃的公司傾向越快進行首次現金增資，顯示市場時機也會影響公司進行首次現金增資的決策。此外，對其首次現金增資之宣告效果進行迴歸分析同時以Heckman Two-Stage Model方法考慮樣本選擇偏誤之修正，結果發現規模越大的公司宣告效果越差而負債比率較大的公司宣告效果越佳。然而上市櫃後進行首次現金增資之時程與其增資宣告效果間則無顯著關係。 / This study examines how fast companies have their first seasonal equity offerings after their IPOs and further analyses the announcement effects of first SEOs. First, we adopt Cox-proportional Hazard Regression Model to see what firm characteristics make IPO firms decide to conduct first SEOs shortly after their IPOs. Using a sample of IPO firms in Taiwan from 1981 to 2010, we find firms that are larger, less profitable and higher growth potential would conduct their first SEOs faster. Also, market timing plays an important role for SEO decisions. Moreover, the announcement effect of their first SEOs shows that elapsed time to conduct first SEOs after IPOs has no influence on the cumulated abnormal returns. By correcting sampling bias, Heckman Two-Stage Model is adopted to reveal better explanation of the results.

開放經濟中，匯率可以透過競爭效果及進口型的通貨膨脹抬升價格，或藉由資產負債效果造成通貨緊縮。本文依循 Carranza et al. (2009) 的實證模型，控制美元化程度的影響，並使用Lin (2010) 的動態分量迴歸方法，針對1974Q1-2010Q4期間80個國家，檢驗不同通貨膨脹水準下的匯率轉嫁效果。總體而言，通膨愈高的時候，匯率貶值的擴張效果愈強；但當通膨降低，其強度也隨之減弱。此結果在考慮其他解釋變數或不同貶值情形後仍維持穩健。而當進一步檢視不同國家或期間的匯率轉嫁效果，匯率對通貨膨脹的正向效果，在中低所得國家中普遍較強，但在1995年後減弱，甚至轉為負向。Taylor（2000）的假說，得以在本文大部分的實證結果中證實。 / In an open economy, exchange rate could either increase prices by competitiveness effect and imported inflation, or be disinflationary through the balance-sheet effect. Controlling for the impact induced by the degree of dollarization, I follow the empirical model of Carranza et al. (2009) with a wide panel of 80 countries over 1974Q1-2010Q4. The exchange rate pass-through is investigated at various inflation levels in a dynamic panel quantile analysis suggested by Lin (2010). In general, exchange rate depreciation is more inflationary the higher inflation levels, but the magnitude of pass-through is reduced as inflation become lower. Also, the results are robust with respect to add other explanatory variables or take the depreciation cases into account. Furthermore, to investigate the pass-through across countries or periods, the positive impact of exchange rate on inflation is greater in middle- and low-income countries, but declines and even becomes negative after 1995. The hypothesis in Taylor (2000) is thus confirmed in most part of our empirical results.

自2003年起隨著台灣壽險業國外投資比率不斷提高，至2010年底國外投資比率已達34.47%，因此為了探討壽險業國外投資與績效並了解相關因素之影響，本研究檢視壽險公司之市占率和各險種保費收入比率與國外投資比率之間的關係，同時亦檢視美國政府十年期公債殖利率與投資報酬率之間是否具有正向關係。另一方面，探討已公開發行公司是否因需揭露財務報表而與未公開發行公司之間在國外投資比率和投資績效上有所差異。 本文以2004年至2008年台灣25家壽險公司的長期資料（longitudinal data），分析總合（pooled）、固定效果(fixed effects)和隨機效果（random effects）迴歸模型，並檢視模型之適合性檢定。另因反應變數之密度估計具長尾之特性，所以亦使用Koenker（2004）和Geraci and Bottai（2007）提出的長期資料分量迴歸（quantile regression for longitudinal data）分析作為探討。實證結果顯示，若壽險公司的市占率愈高，則其資產配置於國外的比重亦相對地提高，且壽險和年金險比率與國外投資比率之間呈現顯著地正相關；此外，公開發行公司的國外投資比率顯著高於未公開發行公司。在投資績效方面，美國政府十年期公債殖利率與投資報酬率之間為顯著的正相關。 長期資料分量迴歸分析實證結果顯示，當使用Koenker（2004）提出之方法時，則一般（ordinary）分量迴歸在50%、75%和90%條件分量下，隨著樣本期間年度的增加，壽險業的國外投資報酬率相對地上升；在10% 和25% 條件分量下，壽險公司市占率與國外投資報酬率之間是顯著的正相關。而使用Geraci and Bottai（2007）提出之隨機效果分量迴歸方法時，在50%條件分量下，國外投資比率與國外投資報酬率之間為顯著地正相關，再者匯率風險將降低台灣壽險業國外投資的意願，然而實行避險策略是有益於投資績效的提升。 / The foreign investment ratio for the life insurance industry in Taiwan has risen constantly since 2003 and reached 34.47% in 2010. In order to explore foreign investment and performance, and understand the impact of relevant factors in the life insurance industry, this study examines the relationship between the market shares of life insurance companies, types of premium income ratio and the foreign investment ratio. Simultaneously, this study also examines the relationship between the 10-year US Treasury Bond Yield Currency and investment return.On the other hand, we explore whether the difference between the publicly traded companies and non-publicly traded companies on the foreign investment ratio and the investment performance. In this dissertation, we analyze 25 Taiwanese life insurance companies between 2004 and 2008 using the pooled, fixed effects and random effects regression model. Due to the distribution of the response variable is characterized by the long tail, we explore the use of the quantile regression for longitudinal data by Koenker(2004)and Geraci and Bottai(2007). The empirical results show that the more market share of life insurance companies, the higher foreign investment ratio and there is significantly positive correlation between the life insurance, annuity ratio and the foreign investment ratio. In addition, the publicly traded company's foreign investment ratio is significantly higher than non-publicly traded company. In terms of investment performance, it’s significantly positive correlation between the U.S. 10-year Treasury Bond Yield Currency and return on investment. The empirical results about quantile regression for longitudinal data show that the return on foreign investment relatively enhance for the life insurance industry with the increase of the year during the sample period under the 50%,75% and 90% conditional qauntile when using the ordinary quantile regression proposed by Koenker(2004). There is significantly positive correlation between the market share and the return on foreign investment under the 10% and 25% conditional qauntile. When using the method proposed by Geraci and Bottai(2007), there is significantly positive correlation between the foreign investment ratio and the return on foreign investment under the 50% conditional qauntile. Furthermore, exchange rate risk will reduce the foreign investment willingness of the life insurance industry in Taiwan. However, the implementation of the hedging strategy is beneficial to enhance investment performance for the life insurance industry.

距離期望值4至6倍標準差以外的罕見機率電流值，是當前積體電路設計品質的關鍵之一，但隨著精確度的標準提升，實務上以蒙地卡羅方法模擬電路資料，因曠日廢時愈發不可行，而過去透過參數模型外插估計或迴歸分析方法，也因變數蒐集不易、操作電壓減小使得電流值尾端估計產生偏差，上述原因使得尾端電流值估計困難。因此本文引進統計方法改善罕見機率電流值的估計：先以Box-Cox轉換觀察值為近似常態，改善尾端分配值的估計，再以加權迴歸方法估計罕見電流值，其中迴歸解釋變數為Log或Z分數轉換的經驗累積機率，而加權方法採用Down-weight加重極值樣本資訊的重要性，此外，本研究也考慮能蒐集完整變數的情況，改以電路資料作為解釋變數進行加權迴歸。另一方面，本研究也採用極值理論作為估計方法。 本文先以電腦模擬評估各方法的優劣，假設母體分配為常態、T分配、Gamma分配，以均方誤差作為衡量指標，模擬結果驗證了加權迴歸方法的可行性。而後參考模擬結果決定篩選樣本方式進行實證研究，資料來源為新竹某科技公司，實證結果顯示加權迴歸配合Box-Cox轉換能以十萬筆樣本數，準確估計左、右尾機率10^(-4) 、10^(-5)、10^(-6)、10^(-7)極端電流值。其中右尾部分的加權迴歸解釋變數採用對數轉換，而左尾部分的加權迴歸解釋變數採用Z分數轉換，估計結果較為準確，又若能蒐集電路資訊作為解釋變數，在左尾部份可以有最準確的估計結果；而篩選樣本尾端1%和整筆資料的方式對於不同方法的估計準確度各有利弊，皆可考慮。另外，1%門檻值比例的極值理論能穩定且中等程度的估計不同電壓下的電流值，且有短程估計最準的趨勢。 / To obtain the tail distribution of current beyond 4 to 6 sigma is nowadays a key issue in integrated circuit (IC) design and computer simulation is a popular tool to estimate the tail values. Since creating rare events via simulation is time-consuming, often the linear extrapolation methods (such as regression analysis) are applied to enhance efficiency. However, it is shown from past work that the tail values is likely to behave differently if the operating voltage is getting lower. In this study, a statistical method is introduced to deal with the lower voltage case. The data are evaluated via the Box-Cox (or power) transformation and see if they need to be transformed into normally distributed data, following by weighted regression to extrapolate the tail values. In specific, the independent variable is the empirical CDF with logarithm or z-score transformation, and the weight is down-weight in order to emphasize the information of extreme values observations. In addition to regression analysis, Extreme Value Theory (EVT) is also adopted in the research. The computer simulation and data sets from a famous IC manufacturer in Hsinchu are used to evaluate the proposed method, with respect to mean squared error. In computer simulation, the data are assumed to be generated from normal, student t, or Gamma distribution. For empirical data, there are 10^8 observations and tail values with probabilities 10^(-4),10^(-5),10^(-6),10^(-7) are set to be the study goal given that only 10^5 observations are available. Comparing to the traditional methods and EVT, the proposed method has the best performance in estimating the tail probabilities. If the IC current is produced from regression equation and the information of independent variables can be provided, using the weighted regression can reach the best estimation for the left-tailed rare events. Also, using EVT can also produce accurate estimates provided that the tail probabilities to be estimated and the observations available are on the similar scale, e.g., probabilities 10^(-5)~10^(-7) vs.10^5 observations.

銅價在近年來不斷的創下歷史新高，由於台灣蓬勃的電子、半導體、工具機產業皆需要銅，因此銅進口量位居全球第五(ICSG,2009)，使得台灣企業的生產成本受國際銅價的波動影響甚鉅，全球有70%的銅價是按照英國倫敦金屬交易所(London Metal Exchange, LME)的牌價進行貿易，因此本研究欲建置預測模式以預測銅價未來趨勢。 　　本研究之資料來源為2003年1月2日至2011年7月14日的LME三月期銅價，並依文獻探討選取LME的銅庫存、三月期鋁價、三月期鉛價、三月期鎳價、三月期鋅價、三月期錫價，以及金價、銀價、石油價格、美國生產者物價指數、美國消費者物價指數、聯邦資金利率作為影響因素的分析資料。時間序列分析、類神經網路已被廣泛的用於預測股市及期貨，本研究先藉由向量自我迴歸模型篩選出有影響力的變數，同時建置GARCH時間序列預測模型與具有遞迴的Elman類神經網路預測模型，再整合兩者建置GARCH-Elman類神經網路預測模型。 本研究之向量自我迴歸模型顯示銅價與金、鋁、銅庫存前第1期；自身前第2期；鎳、錫前第3期；鋅前第4期的變動有負向的影響；受到石油前第2期的變動有正向的影響，這其中以銅的自我解釋變異最高，銅庫存最低，推測其影響已有效率地反映到銅價上。也驗證預測模型必須考量總體經濟變數，且變數先經向量自我迴歸模型的篩選能因減少雜訊而提升類神經網路的預測能力。依此建置的GARCH模型有33.81%的累積報酬率、Elman類神經網路38.11%、整合兩者的GARCH-Elman類神經網路56.46%，皆優於實際銅價指數的累積報酬率。對銅有需求的企業者，能更為準確的預測漲跌趨勢，依此判斷如何跟原物料供應商簽訂合約的價格與期間，使其免於價格趨勢的誤判而提高生產成本，並提出五點建議供未來研究者參考。 / The recent copper price in London Metal Exchange (LME) has breaking the historical high. Taiwan’s booming electronics, semiconductor and machine tool industry causing copper import volume ranked fifth in the world (ICSG, 2009). Because of 70% of copper worldwide trade in accordance with the price of the London Metal Exchange, this study using time series and neural networks to build the LME copper price forecast model. 　　This study considering copper, copper stocks, aluminum, lead, nickel, zinc, tin, gold, silver, oil ,federal funds rate, CPI and PPI during the period of 2003/1/2 to 2011/7/14. Time series model and neural networks have been widely used for forecasting the stock market and futures. In this study, using Vector Autoregressive (VAR) model screened influential variables, building GARCH model and Elman neural network to forecast the LME copper price; and further, integrating this two models to build GARCH-Elman neural network prediction model. 　　This study’s VAR models show that the copper has negative effect with gold, aluminum, copper stocks, nickel, tin, zinc and itself. And has positive impact with oil prices. The highest of explained variance is copper. Copper stocks are lowest, speculating that its impact has been efficiently reflecting on the price of copper. Verifying the prediction model must consider the macroeconomics variables. Using VAR model screened influential variables can reduce noise to enhance the predictive ability of the neural network. This study’s GARCH model has 33.81% of the cumulative rate of return, Elman neural network has 38.11% and the GARCH-Elman neural network has 56.46%. All of them are better than the actual price of copper.

逆物流回收的複雜度遠比正向物流高，企業為專注核心價值多半將逆物流活動委外專業物流服務供應商。對第三方逆物流業者而言，選擇適當的回收處理中心位址為其重要核心能力之一，而現今研究對於選址模式中之回收不確定性，大多以歷史資料作為參數，無根據區域特性不同而有所分別。故本研究希望探討在零售商提供資訊的情境下，結合消費者問卷建構廢棄產品的使用年限機率、並以二元迴歸邏輯分析建構回收機率以此預測區域回收數量，透過資訊分享以建立更好的回收處理中心選址設置模式，使第三方逆物流業者可按照此模式選擇最適當的回收點位置與回收處理量安排用以求得利潤最大化。 / Since reverse logistics is much more complex than forward logistics, third-party logistics providers are often the prior choice for firms to obtain their core value when a reverse logistic activity is needed. For third-party logistics providers, the location is one of their crucial core values; while most of them can only rely on historical data to assume the best location, due to the uncertainty of recycling in present studies.Therefore, this paper tries to construct the probability of products’ used-years by combining the retailers’ information with consumer-oriented questionnaires. Binary logistic regression is the methodology used to analyze and predict recycling probability. By information-sharing the third-party logistics providers will be able to construct a better selecting model for the best facility location, which will reach the most suitable recycling quantity to maximize their profits.

永續發展的概念現今已被運用於都市，其中，都市綠地在環境、生態、景觀、社會各層面之機能皆可提升都市永續性，在快速的都市化下，都市內綠地減少，土地利用變遷帶來之環境衝擊影響已自個體單元累積到全球。然而，過去研究中未有關注在綠地的變化趨勢與其他土地利用間的互動關係，以及在不同區域下的變化差異。因此，本研究採用GIS和景觀指數看在1995年至2006年間台北都會區綠地變遷趨勢，並分區探討土地利用間的互動關係，最後藉由二元羅吉斯迴歸分析綠地變化可能原因。 研究結果顯示，在1995年至2006年間，台北都會區整體發展是建地增加，林地也呈上升趨勢，而草地是土地利用轉移下被犧牲掉最多的土地，綠地轉移成其他土地利用情形以都會邊緣地區最嚴重。不同綠地型態在1995年至2006年間的變遷仍有差異，林地在整個台北都會區屬於景觀中的基質，主導性未受動搖，僅在都會中心減少並受破壞；而農地面積略微下降，呈破碎化發展，尤其以都會中心外圍區農地被破壞情形最明顯；草地面積亦下降，破碎化情形較農地更嚴重，在都會郊區、次中心之草地被破壞嚴重，草地各方面機能降低。透過二元羅吉斯迴歸分析發現自然環境、社會經濟與計畫環境皆影響台北都會區的綠地變遷。根據研究結果，建議未來政府於都市計畫上應將綠地空間納入考量，對於不同綠地型態應有不同管制措施，考量各區域綠地型態之差異性，以及自然環境、社會經濟和計畫環境對於綠地變遷的影響，以促進都市朝向永續發展。 / The concept of sustainable development has been applied in cities. Urban green space plays an important role in enhancing the sustainability of the city in regards to the environment, ecology, landscape and society aspects. Under rapid urbanization, green space has greatly declined in cities. Environmental impact resulting from land use change has grown from local to global proportions. However, researches did not pay attention to interactions between green spaces and other land-use change trends or different types of change in different areas. This research used GIS and landscape metrics to investigate the green space change trend and interactions among different land use types in the Taipei metropolitan area from 1995 to 2006. Furthermore, this research analyzed possible reasons that may have caused green space change through logistic regression. The results showed that, from 1995 to 2006, the built up area and the forest increased in Taipei Metropolitan Area; however, the grass decreased because of land use change. Urban fringe was the place that green space changed to other land-use most. There were differences of land use change for different types of green space. Forest was the matrix in the landscape of Taipei metropolitan area. It still kept the predominant role, only decreased and was destroyed in the center of metropolitan area. Farmland slightly decreased and became fragmented, especially in the periphery of the urban center. Grassland area decreased and became fragmented much more than farmland. In suburb and sub-center, grassland was destroyed seriously and became less functional. Through binary logistic regression, the study found that natural environment, socio-economic and government planning do have influence on green space changes in the Taipei metropolitan area. According to the result of the study, the recommendation was that government should take green space into consideration when doing urban planning. For different types of green space and different areas, the government needs to have different measures and needs to consider the impact factors of green space change in order to accelerate sustainable development in cities.