資產配置策略會影響公司資產負債管理，觀察國內各壽險業者，其資產配置策略大不相同。因此，本研究欲深入分析各保險公司之資產配置策略對公司之影響，並探討保險公司資產配置決策之影響因素，並透過國內外現況加以比較分析。 資產配置策略由公司相關部門依照風險偏好、負債面考量等各項因素訂定投資決策以及目標報酬率並交由經理人執行。而欲瞭解資產配置策略是否完善，本研究採用追蹤資料模型(Panel data model)，以財務績效作為反應變數，並驗證財務績效受到哪些因素影響；此外並以總資產作為分群變數，以集群分析將各公司分群，探討各群內之狀況。 本研究結果發現，公司規模對於稅前股東權益以及稅前每股盈餘有顯著的正向關係，大規模之公司能藉由保險業務之經營創造更高的利潤。人壽保險保費收入比率項對各財務績效指標均無顯著影響；資產風險對投資績效以及稅前每股盈餘有顯著正向影響，此結果顯示提高資產風險能增加投資收益及稅前盈餘；各資產類別對各財務績效指標均有顯著正向影響；不同公司型態間的投資績效以及稅前股東權益報酬率有明顯差異，上市公司之投資績效顯著高於非上市公司；在控制其他變數下，外商公司與本土公司之投資績效無顯著差異，而稅前股東權益報酬率外商公司顯著高於本土公司。稅前每股盈餘資料無法比較不同公司型態間的差異。

本文為探討各地方政府自籌財源之影響因素，首先針對有關地方財政及財政自主性之理論及影響地方自籌財源因素之文獻進行回顧，同時整理臺灣22個縣市可能影響自籌財源相關因素之發展現況，藉以取得適當變數，並將影響地方自籌財源因素分為經濟因素、房地產市場狀況、替代財源、財政能力、稽徵行政因素與政治因素，利用民國91-100年之資料，進行實證分析。本文研究資料涵蓋22個縣市與10個年度，故計量模型採用Panel Data模型，不僅可以分析解釋變數對自籌財源之影響程度，更能透過固定效果模型 (Fixed Effect Model)得知22個縣市之地方特定效果與時間效果。 根據實證結果發現，地方政府的經濟因素、房地產市場狀況、稽徵行政效率與政治因素對地方自籌財源的影響不大，主要影響地方自籌財源的因素為替代財源、財政因素及為無法量化之地方特質與各縣市屬性之不同，以及總體經濟發展與政策。又透過時間效果分析，發現地方自籌財源會隨經濟景氣與房地產市場波動及租稅政策而有所影響。因此，地方財政水平不均為我國財政重要問題，導致有些地方屬性偏遠或農業的縣市，地方財政狀況欠佳，亟須依賴上級政府之補助才得以維持地方事務之推動，且地方自籌財源又易受總體經濟與政策之影響。

台灣各都會區因經濟與社會發展程度不同，使各都會區房地產市場特性有所差異，住宅價格波動情形亦有所不同。過去於台灣雖已有許多文獻探討過區域經濟與社會變數和區域住宅價格之關聯，卻少有文獻討論不同區域彼此間住宅價格差異與區域經濟與社會變數差異關係，且多數探討區域房地產市場文獻亦僅將研究範圍限縮在單一都會區，對於全國都會區之綜合性討論較為缺乏。是故，本文以台灣六大都會區為研究對象，探討各都會區彼此間住宅價格差異時間與空間變動情形，分析其與各都會區彼此間經濟與社會發展差異關係，進一步釐清當中之主要影響因素。 本研究使用台北市、新北市、桃竹都會區、台中都會區、台南都會區與高雄都會區等六大都會區由1993年至2010年共270筆住宅價格兩兩相除之比例資料，透過縱橫資料模型(Panel Data Model)探討國內六大都會區，兩兩間住宅價格比例變動於經濟與社會面的主要影響因素。實證顯示，當兩兩都會區經常性所得、知識密集服務業就業機會、公共投資、交通可及性、辦公室使用執照樓地板面積、治安狀況與空氣品質差異越大，住宅價格差異亦隨之擴張。且各都會區知識密集服務業就業機會、公共投資、交通可及性與經常性所得落差對住宅價格差異影響最大。此外，兩兩都會區住宅價格差異亦受到其地區特性與景氣影響。建議政府可透過於弱勢都會區發展適宜知識密集服務業發展之環境，吸引相關產業進駐，提供當地更多知識密集服務業就業機會，降低國內都會區所得落差。此外，應合理分配各都會區公共投資金額，強化弱勢都會區大眾運輸服務水準，以降低國內各都會區住宅價格懸殊情形。 / In Taiwan, because of the dissimilar levels of urban development, housing prices in different metropolitan areas change in sundry ways. This paper uses panel data analysis to identify the relationship between the development gap and the difference in housing prices in metropolitan areas of Taiwan during 1993-2010. The empirical results reveal that the income gap, the employment of knowledge-intensive services gap, the mobility gap, the public investment gap, the office quantity gap, the public security gap, and the air quality gap had significant effects on the difference in housing prices, and the difference in housing prices is also influenced by local characteristics and real estate cycles. Besides, we also discover that the employment of knowledge-intensive services gap and the public investment gap are two key determinants of the difference in housing prices.

本研究主要探討台北都會區都市蔓延與氣候暖化之關係，實證分析是否都市蔓延的發展形態會造成氣溫的上升。有研究指出台灣的歷年氣溫上升是因為近年來工商業急速發展，人口增加，建築物型態改變，交通運輸量激增等所致。國內外許多研究也發現都市化與氣溫是呈現正相關，而綠地與氣溫呈現負相關。 本研究實證分析部分使用地理資訊系統之內差法和空間分析方法，以及迴歸分析使用panel data之固定效果模型等工具，內插法之結果得到台北都會區年平均氣溫自1996年至2006年約上升1℃，有些地區甚至上升約2℃，且上升之溫度範圍有擴大的趨勢，呈現放射狀的溫度分布，此與都市蔓延之放射狀發展形態類似。使用空間分析方法則證實了一地人口數的增加會造成該地氣溫上升，並且也發現近來人口數多增加在都市外圍地區，這與上述氣溫分布和都市蔓延之放射狀發展形態也相符合。 迴歸分析結果顯示人口數對於氣溫有相當大之正相關，耕地面積對氣溫則呈現負相關，可見得擁有廣大綠地可以降低區域之氣溫，減緩氣候暖化，因此建議政府需檢討當前農地政策，配合環境保護，適合時宜的提出正確之政策。另外在各鄉鎮市區固定效果估計量方面，可以歸納出若一地區有廣大的公園、綠地、或是有河川流域的經過，對於降低當地氣溫有明顯的幫助；時間趨勢之固定效果估計量顯示台北都會區隨著時間的經過，氣溫將持續上升。因此在未來都市規劃方面，規劃者必須了解各地區特性，善加利用其自然環境以調和氣候暖化之影響、多設置公園綠地、多種植綠色植物、在道路周邊行道樹的設置、建築物間風場之設計等。如此將可以降低都市蔓延對氣候暖化的影響，以及防止氣候暖化的發生。 / In this study, we research the relationship between urban sprawl and climate warming in Taipei metropolitan area. Analyze empirically whether the developed shape of urban sprawl causes the climbing of the temperature. Some studies indicate that the reasons why the climate is getting warmer in Taiwan are the high-speed developments of industry and commerce, the increase of population, the changes of the buildings and the huge increase of the traffic volume. Some other studies also find out that there is a positive correlation between the urbanization and the temperature, and there is a negative correlation between the green space and the temperature. The empirical analysis in this study is based on the Interpolation Method and Spatial Analysis of GIS. And the regression analysis is based on the Fixed Effect Model of Panel Data. The yearly average temperature increased about 1℃ to 2℃ in the Taipei metropolitan area from 1996 to 2006. Furthermore, the range of the increasing temperature has been trending up, and it reveals a radial distribution. It is similar to the radial developed shape of urban sprawl. By using Spatial Analysis, we prove that the temperature of an area increases when the population rises. And we find out that the population rises in most of the peri-urban areas. It also answers to the radial developed shape of urban sprawl and the distribution of the temperature as above. The result of using the regression analysis shows that there is a positive correlation between the number of the population and the temperature and is a negative correlation between the farmland areas and the temperature. So that if there is a big green space, it can decrease the temperature in an area, reduce climate warming. For this reason, I suggest that the government should review our current farmland policy, which should be worked with the environmental protection policy, and bring it into practice at the right time right place. From the fixed effect estimation, we concludes that it helps decrease the temperature in an area obviously when there is a big park, big green space or where a river passing through. The time trend of the fixed effect estimation indicates that the climate in the Taipei metropolitan area will be getting warming with time goes by. Therefore, the urban planner should know better of the feature in each area, using the natural environment to accommodate the influence of climate warming. To have more parks, green spaces and plants, plant more trees by the roads, design the wind flow between buildings. Cut down the carbon production by using either way. Thus and so, we can reduce the influence of urban sprawl to climate warming, and also prevent climate warming.

人民幣是否可能成為另一個重要的國際貨幣，甚至挑戰美元的國際地位？此即本論文的問題意識。對此，本論文進一步提出三個研究問題：一是如何測量當前的人民幣國際化程度？二是如何測量當前的人民幣資本開放程度？三是資本開放對於人民幣國際化程度的影響為何？ 為此，本研究利用主成分分析（PCA），以建構人民幣國際化程度（CIDI）與人民幣資本帳開放程度（CAOI）。其次再利用動態追蹤資料模型──系統一般動差估計法（SGMM），以檢證各項人民幣綜合競爭力對於貨幣國際化程度的影響。最後，本研究進一步梳理人民幣資本帳開放的進程，並結合上述所有實證分析的結果，進而預估漸進資本開放下人民幣國際化的前景。研究對象包括人民幣在內的33種國際貨幣，研究時間則起自1999年歐元成立，迄於2009年。 本論文的發現三：一是，當前人民幣國際化程度進展相當快速。但截至2009年年底，人民幣國際化程度還很低，遠落後於美元、歐元、日圓，以及英鎊等主要國際貨幣。不僅如此，人民幣國際化程度也遜於俄羅斯盧布、巴西里拉，以及印度盧比等開發中國家所發行的貨幣。 二是，過去10年來，人民幣資本帳開放程度不升反降，截至2009年年底，人民幣的資本帳開放程度維持在零，這表示：人民幣是世界上管制最為嚴格的貨幣。相對而言，美元、歐元、日圓，以及英鎊的資本帳開放程度至少都在70％以上，特別是英鎊的資本帳開放程度更趨近於完全開放。 三是，根據SGMM的實證結果顯示，網路外部性、經濟規模、金融市場規模、貨幣穩定度，以及資本開放程度都是影響貨幣國際化程度的關鍵因素。在此基礎上，本研究利用發生機率（odds ratio），以計算不同資本開放情境下，人民幣成為前10大國際貨幣的可能性。結果顯示，如果人民幣的資本帳開放到73％左右，人民幣便可擠進前10大國際貨幣（發生機率為65.6％）。 不過，這只是最為保守的估計。原因有二：一是，隨者中國經濟實力的崛起，以及人民幣預期升值的脈絡下，國際市場對於人民幣的需求原本就很高。此時，人民幣資本帳如果能適時開放，則人民幣的國際持有將大幅增加。換言之，本研究沒有考量到，各貨幣競爭力因素與資本開放程度之間的加乘效果。 二是，資本開放不僅直接對貨幣國際化程度產生影響，也會透過擴大金融市場規模與網路外部性等其他貨幣競爭力因素，間接對貨幣國際化程度造成影響。這間接效果，本研究也沒有考量到。因此，可以預期的是，只要人民幣資本帳能夠漸進開放，人民幣國際化的前景將比本研究所預估的高出許多。 / This paper discusses whether the Renminbi (RMB) will become an international currency, even challenging to the U.S. dollar. In order to examine above question, this paper take the following three steps: 1. By using principal component analyses (PCA), this paper constructs two indices: currency internationalization degree index (CIDI) and capital account liberalization degree index (CAOI); 2. By using dynamic panel data model-system generalized method of moment (SGMM), this paper analyzes factors affect the CIDI, including economic and trade size, financial system, network externalities, confidence in the currency’s value, and CAOI; 3. According to the PCA and SGMM results, this paper calculates the odds ratio of RMB becoming important international currency. The reserch achieved the following results. First, the degree of internationalization of the RMB progress very fast, but the RMB CIDI is still very low, its CIDI far behinds the dollar, euro, Japanese yen, and pounds. Second, over the past 10 years, RMB CAOI is not increased but decreased. Its CAOI is at zero in 2009, this means that: the RMB is the most stringent controls in the world currency. In contrast, U.S. dollars, euros, yen, and pound CAOI are at least in more than 70%. Third, according to the SGMM results, economic size, financial system, network externalities, confidence in the currency’s value, and CAOI are key factors affect the CIDI. Based on this output, this paper forecasted that if the RMB CAOI is open to about 73%, RMB could be squeezed into the top 10 of the international currency. (The odds ratio is 65.6%) It is noteworthy that this is only the lowest estimates. This is because that this paper did not consider the interaction effects of each currency competitiveness factors and CAOI. Therefore, if RMB CAOI continues open, the prospect of RMB CIDI is much higher than estimated by this paper.

近年來，全球氣候變遷與環境惡化顯示目前人類活動與永續發展的衝突，如何減少能源消耗以降低溫室氣體排放成為當務之急。在都市規劃方面，以緊密都市理念探討都市發展與交通能源消耗最受到關注，然而都市發展與產業活動、家戶行為密切相關，因此若僅從交通方面分析能源消耗，恐忽略其他影響能源消耗的重要因素。故本研究著重於探討都市發展特性與能源消耗的關聯性，將產業活動與家戶行為納入模型分析，可更全面性了解都市與能源消耗的關聯性。此外，基於「全球思考、地方行動」的考量，如何落實節能減碳的目標，必須就各縣市的都市發展特性著手，透過追蹤資料模型，進一步了解各縣市的固定效果對能源消耗之影響以及能源消耗之時間趨勢。 透過對都市耗能部門進行分類，並以台灣改制後19個縣市為實證範圍，可以確認都市發展特性的幾個面向：土地使用密度、土地混合使用、交通屬性、產業屬性、家戶屬性、環境屬性與交通運輸、產業發展、家戶活動的耗能關係。實證結果發現，緊密都市有助於節能目標達成；道路增加可及性，但亦助長汽車使用，增加交通耗能；為提升大眾運輸使用率，需加強轉乘便利性以及改變私人交通運輸偏好；產業耗能與出口貿易關聯性高，不利能源減量；家戶居住行為與生活型態對能源消耗有正向影響；公園綠地能調節都市氣溫，減少耗能。 綜上所述，欲形塑一個節能減碳的都市必須透過多元途徑，在土地使用方面須維持適當的發展密度與混合程度；交通方面須加強大眾運輸便利性以提高民眾使用意願並抑制汽車使用；產業方面須透過政府獎勵節能措施及促進產業轉型以提升能源效率；家戶方面應透過教育及政策宣導以培養節能生活習慣，方能達成節能減碳的目標。 / In recent years, global climate change and environmental deterioration show a conflict between human activities and sustainable development. How to reduce energy consumption in order to mitigate greenhouse gas emission has become a top priority. According to the concept of compact city, urban planning is seen as the effective way to reduce transportation energy consumption. However, urban development is associated with industry development and household activities, so it would be improper to focus only on transportation sector. Thus the main motivation for this study is trying to illustrate urban development characteristics by combing transportation sector, industry sector and household sector, in order to understand the influence the urban development characteristics on energy consumption more comprehensively. In addition, on the concept of “global thinking, local action”, how to successfully implement energy saving policies should first understand the urban development characteristics of all counties. The purpose of this study is to empirically explore the influences of urban development characteristics on energy consumption by using panel data models, which uses the reorganization of nineteen counties areas in Taiwan as samples.In order to find out the fixed effect of all counties on energy consumption and trend of energy consumption. The empirical results show that the concepts of compact city still contributes the energy-saving goal；construction of roads increase accessibility, but also encourage car use, which increase energy consumption；to encourage the use of public transportation need to improve the convenience of transfer and change the preference of people；energy consumption in industry is highly associated to international trade, so it would be difficult for energy reduction；the trend of energy consumption has increased due to household lifestyle change；the green resources provides by park, which can adjust the temperature of city and reduce energy consumption. In sum, achieving the energy-saving city need diversified approaches, it can’t just keep increasing the density or land mixed-use. Traffic should be strengthened by improving transfer system. In order to increase the willingness to use public transport system and decrease car dependency；industry must trying to improve energy efficiency；households should cultivate the habit of saving energy by education in order to be a true energy-saving city.

本論文的研究主軸圍繞於自我迴歸模型的估計與推論上。文獻上自我迴歸模型的估計多直接採用最小平方法, 但此估計方式卻有兩個缺點:(一)當序列具單根時,最小平方估計式的漸近分配為非正規型態,因此檢定時需透過電腦模擬得到臨界值;(二)最小平方估計式雖具一致性,但卻有嚴重的有限樣本偏誤問題。有鑑於此,我們提出一種「二階差分轉換估計式」,並證明該估計式的偏誤遠低於前述最小平方估計式,且在序列為粧定與具單根的環境下具有相同的漸近常態分配。此外,二階差分轉換估計式相當適合應用於固定效果追蹤資料模型,而據以形成的追蹤資料單根檢定在序列較短的情況下仍有不錯的檢定力。 本論文共分四章,茲分別簡單說明如下: 第1章為緒論,回顧文獻上估計與推論自我回歸模型時的問題,並說明本論文的研究目標。估計自我迴歸模型的傳統方式是直接採取最小平方法,但在序列具單根的情況下由於訊息不隨時間消逝而快速累積,使估計式的收斂速度高於序列為恒定的情況。不過,這也導致最小平方估計式的漸近分配為非標準型態,並使得進行假設檢定前必須先透過電腦模擬來獲得臨界值。其次,最小平方估計式雖具一致性,但在有限樣本下卻是偏誤的。實證上, 樣本點不多是研究者時常面臨的窘境,並使得小樣本偏誤程度格外嚴重。本章中透過對前述問題形成因素的瞭解,說明解決與改善的方法,亦即我們提出的「二階差分轉換估計式」。 第2章主要目的在於推導二階差分轉換估計式之有限樣本偏誤。我們亦推導了多階差分自我迴歸模型下二階段最小平方估計式(two stage least squares, 2SLS)與 Phillips andHan (2008)採用的一階差分轉換估計式之偏誤,以同時進行比較。本章理論與模擬結果皆顯示,一階與二階差分轉換估許式與2SLS之 $T^{−1}$ 階偏誤程度皆低於以最小平方法估計原始準模型(level model)的偏誤,其中 T 為時間序列長度。另外,一階差分轉換估計式與二階差分轉換估計式在 $T^{−1}$ 階偏誤上,分別與一階和二階差分模型下2SLS相同,但兩估計式的相對偏誤程度則因自我相關係數的大小而互有優劣。同時,我們發現估計高於二階的差分模型對小樣本偏誤並無法有更進一步的改善。最後,即使在樣本點不多的情況下,本章所推導的偏誤理論對於實際偏誤仍有良好的近似能力。 第3章主要目的在於發展二階差分轉換估計式之漸近理論。與 Phillips and Han (2008) 採用之一階差分轉換估計式相似的是,該估計式在序列為恒定與具單根的情況下收斂速度相同,並有漸近常態分配的優點。值得注意的是, 二階差分轉換估計式的漸近分配為 N(0,2),不受任何未知參數的影響。另外,當序列呈現正自我相關時,二階差分轉換估計式相較於一階差分轉換估計式具有較小的漸近變異數,進而使得據以形成的檢定統計量有較佳的對立假設偵測能力。最後, 誠如 Phillips and Han (2008) 所述,由於差分過程消除了模型中的截距項,使得此類估計方法在固定效果的動態追蹤資料模型(dynamic panel data model with fixed effect) 具相當的發展與應用價值。 本論文第4 章進一步將二階差分轉換估計式推展至固定效果的動態追蹤資料模型。文獻上估計此種模型通常利用差分來消除固定效果後,再以一般動差法 (generalized method of moments, GMM) 進行估計。然而,這樣的估計方式在序列為近單根或具單根時卻面臨了弱工具變數(weak instrument)的問題,並導致嚴重的估計偏誤。相反的,差分轉換估計式所利用的動差條件在近單根與單根的情況下仍然穩固,因此在小樣本下的估計偏誤相當輕微(甚至無偏誤)。另外,我們證明了不論序列長度(T )或橫斷面規模(n)趨近無窮大,差分轉換估計式皆有漸近常態分配的性質。與單一序列時相同的是,我們提出的二階差分轉換估計式在序列具正自我相關性時的漸近變異數較一階差分轉換估計式小;受惠於此,利用二階差分轉換估計式所建構的檢定具有較佳的檢力。值得注意的是,由於二階差分轉換估計式在單根的情況下仍有漸近常態分配的性質,我們得以直接利用該漸近理論建構追蹤資料單根檢定。電腦模擬結果發現,在小 T 大 n 的情況下,其檢力優於文獻上常用的 IPS 檢定(Im et al., 1997, 2003)。 / This thesis deals with estimation and inference in autoregressive models. Conventionally, the autoregressive models estimated by the least squares (LS) procedure may be subject to two shortcomings. First, the asymptotic distribution of the LS estimates for autoregressive coefficient is discontinuous at unity. Test statistics based on the LS estimates thus follow nonstandard distributions, and the critical values obtained need to rely on Monte Carlo techniques. Secondly, as is well known, the LS estimates of autoregressive models are biased in finite samples. This bias could be substantial and leads to serious size distortion for the test statistics built on the estimates and forecast errors. In this thesis,we consider a simple newmethod ofmoments estimator, termed the “transformed second-difference” (hereafter TSD) estimator, that is without the aforementioned problems, and has many useful applications. Notably, when applied to dynamic panel models, the associated panel unit root tests shares a great power advantage over the existing ones, for the cases with very short time span. The thesis consists of 4 chapters, which are briefly described as follows. 1. Introduction: Overview and Purpose This chapter first reviews the literature and states the purpose of this dissertation. We discuss the sources of problems in estimating autoregressive models with the conventional method. The motivation to estimate the autoregressive series with multiple-difference models, instead of the conventional level model, is provided. We then propose a new estimator, the TSD estimator, which can avoid (fully or partly) the drawbacks of the LS method, and highlight its finite-sample and asymptotic properties. 2. The Bias of 2SLSs and transformed difference estimators in Multiple-Difference AR(1) Models In this chapter, we derive approximate bias for the TSD estimator. For comparisons, the corresponding bias of the two stage least squares estimators (2SLS) in multiple-difference AR(1) models and the transformed first-difference (TFD) estimator proposed by Chowdhurry (1987) are also given as by-products. We find that: (i) All the estimators considered are much less biased than the LS ones with the level regression; (ii)The difference method can be exploited to reduce the bias only up to the order of difference 2; and (iii) The bias of the TFD and TSD estimators share the same order at $O(T^{-1})$ as that of 2SLSs. However, to the extent of bias reductions, neither the 2 considered transformed difference estimators shows a uniform dominance over the entire parameter space. Our simulation evidence lends credible supports to our bias approximation theory. 3. Gaussian Inference in AR(1) Time Series with or without a Unit Root The goal of the chapter is to develop an asymptotic theory of the TSD estimator. Similar to that of the TFD estimator shown by Phillips and Han (2008), the TSDestimator is found to have Gaussian asymptotics for all values of ρ ∈ (−1, 1] with $\sqrt{T}$ rate of convergence, where ρ is the autoregressive coefficient of interest and T is the time span. Specifically, the limit distribution of the TSD estimator is N(0,2) for all possible values of ρ. In addition, the asymptotic variance of the TSD estimator is smaller than that of the TFD estimator for the cases with ρ > 0, and the corresponding t -test thus exhibits superior power to the TFD-based one. 4. Estimation and Inference with Moment Methods for Dynamic Panels with Fixed Effects This chapter demonstrates the usefulness of the TSD estimator when applying to to dynamic panel datamodels. We find again that the TSD estimator displays a standard Gaussian limit, with a convergence rate of $\sqrt{nT}$ for all values of ρ, including unity, irrespective of how n or T approaches infinity. Particularly, the TSD estimator makes use of moment conditions that are strong for all values of ρ, and therefore can completely avoid the weak instrument problem for ρ in the vicinity of unity, and has virtually no finite sample bias. As in the time series case, the asymptotic variance of the TSD estimator is smaller than that of the TFD estimator of Han and Phillips (2009) when ρ > 0 and T > 3, and the corresponding t -ratio test is thus more capable of unveiling the true data generating process. Furthermore, the asymptotic theory can be applied directly to panel unit root test. Our simulation results reveal that the TSD-based unit root test is more powerful than the widely used IPS test (Im et al, 1997, 2003) when n is large and T is small.