Yes / So far, the dominant comparative studies of competing distress prediction models (DPMs) have been restricted to the use of static evaluation frameworks and as such overlooked their performance over time. This study fills this gap by proposing a Malmquist Data Envelopment Analysis (DEA)-based multi-period performance evaluation framework for assessing competing static and dynamic statistical DPMs and using it to address a variety of research questions. Our findings suggest that (1) dynamic models developed under duration-dependent frameworks outperform both dynamic models developed under duration-independent frameworks and static models; (2) models fed with financial accounting (FA), market variables (MV), and macroeconomic information (MI) features outperform those fed with either MVMI or FA, regardless of the frameworks under which they are developed; (3) shorter training horizons seem to enhance the aggregate performance of both static and dynamic models.

近年來，隨著經濟發展、國民所得提升，再加上政府政策的協助推廣，刺激了國內觀光旅遊市場之需求，造成業者競相投入此一產業。然而，在市場競爭日益激烈的環境下，國際觀光旅館的需求卻未明顯地成長，呈現出供過於求的狀態。面對這種情形，為求國際觀光旅館之永續經營與成長，惟有追求生產力的不斷提升。因此，國際觀光旅館生產力變動的情形值得做進一步地探討。本文以1997-2002年台灣國際觀光旅館之整合資料，首先利用Malmquist生產力指數衡量歷年生產力變動之情形；然後，以Bootstrap信賴區間法加以檢定，藉以瞭解Malmquist生產力指數是否有顯著成長或衰退的現象發生。由Malmquist生產力指數評估結果發現：就整個研究期間而言，生產力及效率皆略為提升；但生產技術則呈現微幅下降。此外，若進一步將國際觀光旅館區分為不同經營型態時，其結果顯示：整體期間連鎖經營型態之國際觀光旅館，不論是在生產力、技術或效率變動，皆優於非連鎖經營型態之國際觀光旅館。由Bootstrap信賴區間法之檢定結果發現：在利用Bootstrap信賴區間法檢定Malmquist生產力指數後，其結果可能截然不同。因此，單憑一個Malmquist生產力指數就輕易斷言生產力提升、不變或衰退，可能會發生誤判的情形。

The NZ dairy industry has adopted an encompassing measure of performance, total factor productivity (TFP), as a target measure to guide on-farm improvements. Dairy farmers pay a levy in order to fund agricultural research and extension. Extension services and R&D will continue to be of critical importance to maintain and improve productivity at the farm level. Consequently, it is in the best interest of the dairy industry to adequately target R&D and extension funds and make the best use of resources. To date, the methodology employed to estimate productivity growth has some shortcomings that seriously hamper the ability of potential users to extract useful information from it. First, productivity growth has been reported as an aggregate for the entire dairy industry. Second, it makes no assumption about the efficiency with which resources are being used. Third, it implicitly assumes that all farms face the same technology. Productivity growth can be achieved either through better (more efficient) use of the technology applied, through the adoption of a new technology (technical progress) or a combination of both. Given that the sources of productivity change—technical progress and technical efficiency change—are fundamentally different phenomena, they are, in turn, influenced by different factors. This distinction is important for policy orientation because different instruments/tools may be required to address them. Furthermore, empirical evidence suggests that a variety of farming systems have emerged as a result of dairy farming geographical expansion. Farm-level panel data were used to estimate the Malmquist productivity change index. This index can provide additional insights since it can be decomposed into two additional components, one that measures changes in technical efficiency (i.e., whether firms are getting closer to the production frontier over time), and one that measures changes in technology (i.e., whether the production frontier is moving outwards over time). Hence, it provides individual (farm) estimates of TFP. Moreover, the methodology applied allows to test whether farms in the two regions considered in this study are operating under the sametechnology. These two regions were the long-established dairy areas of Waikato-Taranaki and the newly developed dairy areas of Canterbury-Southland. Results for farms in Waikato-Taranaki indicate that annual TFP change is modest, ranging from 0.29% per annum to 0.59% per annum. Most importantly, technical progress is the only source of TFP change in all four models. Therefore, it is necessary to encourage investments in new R&D targeted to remove the technological constraints that impede the realisation of further productivity gains in the regions. However, important differences in the estimates of TFP, technical progress and change in technical efficiency between models were found for farms in Canterbury-Southland. Estimates of TFP change ranged from 0.7% per annum to 2.8% per annum. Even though technical progress and change in technical efficiency contributed to total factor productivity growth (TFPG), the latter component was the most important contributor in three of the four models. Moreover, in two models the rate of technical progress was negative (i.e., technical regress). The analyses indicate that dairy farms in Canterbury-Southland were on average 10% more productive than farms in Waikato-Taranaki when farms in both regions faced the frontier. These results were consistent for all the input/output set chosen. Furthermore, the null hypothesis that the two regions do not face the same production technology (i.e., each region has it own production frontier) was accepted irrespective of the input/output set chosen. The rejection of the null hypothesis, that farms in traditional and non-traditional dairy regions were operating under the same underlying technology (and hence face the same production frontier), called for a review of the traditional approach to R&D in one central experimental station, strengthening the need for a local approach through the promotion of networks and synergies with universities and other research institutions.

The NZ dairy industry has adopted an encompassing measure of performance, total factor productivity (TFP), as a target measure to guide on-farm improvements. Dairy farmers pay a levy in order to fund agricultural research and extension. Extension services and R&D will continue to be of critical importance to maintain and improve productivity at the farm level. Consequently, it is in the best interest of the dairy industry to adequately target R&D and extension funds and make the best use of resources. To date, the methodology employed to estimate productivity growth has some shortcomings that seriously hamper the ability of potential users to extract useful information from it. First, productivity growth has been reported as an aggregate for the entire dairy industry. Second, it makes no assumption about the efficiency with which resources are being used. Third, it implicitly assumes that all farms face the same technology. Productivity growth can be achieved either through better (more efficient) use of the technology applied, through the adoption of a new technology (technical progress) or a combination of both. Given that the sources of productivity change—technical progress and technical efficiency change—are fundamentally different phenomena, they are, in turn, influenced by different factors. This distinction is important for policy orientation because different instruments/tools may be required to address them. Furthermore, empirical evidence suggests that a variety of farming systems have emerged as a result of dairy farming geographical expansion. Farm-level panel data were used to estimate the Malmquist productivity change index. This index can provide additional insights since it can be decomposed into two additional components, one that measures changes in technical efficiency (i.e., whether firms are getting closer to the production frontier over time), and one that measures changes in technology (i.e., whether the production frontier is moving outwards over time). Hence, it provides individual (farm) estimates of TFP. Moreover, the methodology applied allows to test whether farms in the two regions considered in this study are operating under the sametechnology. These two regions were the long-established dairy areas of Waikato-Taranaki and the newly developed dairy areas of Canterbury-Southland. Results for farms in Waikato-Taranaki indicate that annual TFP change is modest, ranging from 0.29% per annum to 0.59% per annum. Most importantly, technical progress is the only source of TFP change in all four models. Therefore, it is necessary to encourage investments in new R&D targeted to remove the technological constraints that impede the realisation of further productivity gains in the regions. However, important differences in the estimates of TFP, technical progress and change in technical efficiency between models were found for farms in Canterbury-Southland. Estimates of TFP change ranged from 0.7% per annum to 2.8% per annum. Even though technical progress and change in technical efficiency contributed to total factor productivity growth (TFPG), the latter component was the most important contributor in three of the four models. Moreover, in two models the rate of technical progress was negative (i.e., technical regress). The analyses indicate that dairy farms in Canterbury-Southland were on average 10% more productive than farms in Waikato-Taranaki when farms in both regions faced the frontier. These results were consistent for all the input/output set chosen. Furthermore, the null hypothesis that the two regions do not face the same production technology (i.e., each region has it own production frontier) was accepted irrespective of the input/output set chosen. The rejection of the null hypothesis, that farms in traditional and non-traditional dairy regions were operating under the same underlying technology (and hence face the same production frontier), called for a review of the traditional approach to R&D in one central experimental station, strengthening the need for a local approach through the promotion of networks and synergies with universities and other research institutions.

At first, the basics of microeconomics from the perspective of companies, effectiveness and methods of its measurement and the most important information on the distance function this thesis, are shown. It also contains the development of the Malmquist productivity index. The aim of this work is the description of this index and its components. Indicate how these values are calculated and what expressed. Secondary objectives are to introduce different variants of Malmquist indexes and their use. Four models are used for calculating individual components of the Malmquist productivity index. These are the DEA models, Aigner-Chu, Stochastic production frontiers and Stochastic activity analysis. The first three in this work are described in detail. In conclusion is an illustrative example of calculation Malmquist productivity index using DEA models. For the calculation I used the program Lingo.

近年來台灣筆記型電腦產業，在廠商延續過去多年的擴張競爭力下，在產值與產量上皆有大幅度的成長。但也由於低價風潮的盛行與全球景氣蕭條，使得該產業的獲利大不如從前；加重了國內廠商經營的困境與競爭程度，此時惟有掌握經營績效之廠商才可維持競爭優勢；以因應高度的競爭環境。在此同時，南韓已從金融風暴的創傷下逐漸走出；並挾帶著全球最大TFT-LCD及DRAM的量產國，並從輕薄型機種切入市場以及加入代工訂單的爭奪戰中。而日本擁有全球頂尖的設計創新能力，筆記型電腦自製率仍偏高。因此日、韓一直是台灣的主要競爭對手。 首先藉由資料包絡分析法評估我國筆記型電腦商於2000年至2003年間的經營效率；並為彌補DEA無法做跨時期的分析，應用Malmquist生產力指數予以分析；接著用Tobit迴歸分析來探討廠商無效率的原因。得到以下的結論： (1)於這4年間的技術效率平均值呈現年年上揚的情況，且規模較大的廠商不意謂著經營績效會較好。(2)這4年間的生產力亦是逐年提昇，主要是技術變動所致。(3)在Tobit迴歸分析中，存貨週轉率、總資產週轉率與品牌對效率有正向影響；而TCRI有負向影響。 接著對台、日、韓進行比較分析，得到以下結論：(1)台、日、韓三國的經營績效，以日本最優、韓其次、台最差；但日、韓的整體效率值逐年下滑，而台灣有逐漸改善的趨勢。(2)三國於2000~2001年間生產力皆下降，這與全球景氣蕭條及九一一事件有相當的關聯；2001~2002間由於無線上網的新技術致使消費性機種熱賣，因此於該期間，生產力呈提昇現象。(3)由Tobit迴歸分析得知存貨週轉率、總資產週轉率與經營年限對效率值有正向影響。 / In the recent years, Taiwanese Notebook manufacturers last the past years, extensions to make productive values and production grow up enormously. Due to the low price trend and global business trend stagnancy, resulting to profitability be not as good as the before for this industry. It deepens to operate and complete hard for the domestic manufacturers. At the same time, only the manufacturers that seizing operating the performance will maintain completing advantage to deal with highly completing enviornment. At the same time, South Korea has already got over from the shadow of 1998 crisis; and took along the most global productive country of TFT-LCD and DRAM; going into the market of frivolous type and join a battle of OEM/ODM orders. Japan owns global excellent design and capability of innovation and self-made ratio is still high. Therefore, Japan and South Korea are constantly Taiwanese major rivals. Firstly, it estimates how Taiwan Note Book manufacturers operate in 2000 to 2003 by Data Envelopment Analysis;In order to making up DEA not undertaking time-series analysis, using Malmquist Productivity Index to analyze; utilizing Tobit regression to analyze the factors that producers operate inefficiently. It gets the following conclusions:(1) In these four years, average technical efficiency is taken on rising year by year, and large scale producers do not imply better operating efficiency. (2) In these four years, the productivity is also rised year by year. This is due to technical change. (3) In Tobit regression analysis, there is a positive influence that inventory turnover, total asset turnover and brand affect efficiency; there is a negative influence that TCRI affects efficiency. This study gets the following conclusions;(1) For Taiwanese, Japanese and South Korea’ operating performance, Japan is the excellent, South Korea is the next, Taiwan is the worst. But Japanese and South Korea’ overall efficiency is worse year by year; Taiwan operating performance is gradually improved. (2) These three countries’ productivity decline from 2000 to 2001. It is concerned with global economic recession and 911 incident. The productivity is raised from 2001 to 2002. It results from WLAN type to sell briskly. Turnover ratio of inventory and turnover ratio of total asset tighter operating years all have a positive influence on efficiency.

台灣產業的發展一向與國際市場的脈動相連，光電產業也不例外，近幾年來在國際的夾擊之下依然發展得極為出色，與其他部分高科技產業共同支撐起台灣科技產業的命脈。所以本研究藉著資料包絡分析法 (data envelopment analysis, DEA) 的應用，透過計算整體技術效率、純粹技術效率、規模效率與 Malmquist 生產力指數，具體衡量出各廠商的生產效率值與跨期變化情形以供廠商參考。另外將光電產業依產品特性分成幾個次產業，為各次產業提出客觀的經營建議。最後由 Tobit 迴歸分析影響效率的重要因素，提供提升效率的參考，希望能對我國的光電產業有所幫助，而繼續為台灣科技產業的成長努力。 本研究以國內 30 家光電產業廠商於 2000 年至 2003 年之資料為研究範圍，選取的投入變數為：員工人數、固定資產、營業成本與營業費用四項，產出變數為營業收入淨額與稅前淨利。另外迴歸分析中使用的解釋變數為董監員工紅利率、存貨週轉率、負債比率、研發費用率、 TCRI 信用評等與董監持股比率。 研究結果顯示，整個光電產業整體無效率的原因較多是技術無效率導致，小部份是規模無效率造成的；而就每個次產業而言，以其四年平均值來看亦是受技術無效率影響較多。而在 Malmquist 生產力指數分析中發現，整個光電產業只有在 2000 年至 2001 年間，因技術退步的影響大於綜合技術效率進步的影響，而使得生產力衰退，往後的期間皆呈現進步的態勢。而在次產業方面，綜合技術效率的退步導致光通訊產業在 2001 年至 2002 年間的生產力衰退；技術的退步使得 2002 年至 2003 年間的光輸出入產業產生生產力的衰退。 最後，在迴歸分析中的結果為存貨週轉率對於整體技術效率有著顯著的正向影響；研發費用率是顯著的負向影響。而在純粹技術效率部份，存貨週轉率有顯著正向影響；TCRI評等越佳，TCRI 值越低，純粹技術效率越好。 / Taiwan's industries are always connected closely with international market, and optoelectronic industry is no exception. Through keenly competence these years, it is still brilliant and boosts the lifeline of Taiwan's Technology industry with other Hi-tech industries. So this research uses data envelopment analysis as objective references to enhance production efficiency. First we divide the whole optoelectronic industry into several sub-industries according to the characteristics of their products, and calculate every firm and sub-industry’s values of technical efficiency, pure technical efficiency, scale efficiency and Malmquist index definitely. Then we analyze the key factors influencing production efficiency and offer references for helping optoelectronic industry better. This research is based on the data of 30 optoelectronic firms in Taiwan during the 2000-2003 periods. We use the number of employee, fixed assets, operating costs and operating expenses as input variables; net sales revenue and net income before taxes as output variables. Besides, the explanatory variables in regression analysis are the percentage of bonus accounting for operating net amount, inventory turns, debt ratio, the percentage of R&D expenses accounting for operating net amount, TCRI, share holding on directors. The result shows that inefficiency in the whole industry and sub-industries mainly comes from technical inefficiency. MPI demonstrates that productivity of the whole industry is declined only during 2000-2001 period and it is because of declined technology. And declined technical efficiency results in the declined productivity of fiber-communication industry between 2001-2002; declined technology results in the declined productivity of optical input/output devices industry between 2002-2003. Under regression analysis, the result shows that inventory turnovers have a significant positive effect on efficiency; the percentage of R&D expenses accounting for operating net amount has a significant negative effect on efficiency. And about pure technical efficiency, inventory turn has a significant positive effect; TCRI has a significant negative effect.

台灣的IC設計產業總產值全球僅次於美國，自從九零年代中期成為我國極受矚目的產業。因此本研究所探討的主題為全球IC設計產業的生產效率分析，以2003年全球營收前卅大IC設計廠商為樣本。透過資料包絡法(Data Envelopment Analysis，DEA)，針對不同地區與個別廠商進行2000年至2002年的效率分析，最後進行Tobit迴歸分析，求得影響廠商生產績效的因子，提供本國廠商改善效率的參考，實證結果為： 1.DEA跨國比較的結果顯示，在2000年至2002年整體技術效率值最高的地區為美國，在2000年與2001年為台灣整體技術效率值僅次於美國，但在2002年卻落居第三。觀察個別廠商績效，Qualcomm、MediaTek、Marvell、ICS、Lattice、Pmc-Sierra與DSP Group等廠商績效最好，其整體技術效率值為1。 2.Malmquist生產力指數(MPI)跨國比較的結果顯示，臺、美、加三國總要素生產力均呈現退步的情形，2001至2002年，臺、加總要素生產力呈現進步，美國呈現退步。就個別廠商而言，在2000年至2001年間有四家廠商總要素生產力進步，在2001至2002年年則有十五家廠商總要素生產力呈現進步的狀況。 3.Tobit迴歸分析的結論為：存貨週轉率inventory turnover對整體技術效率有顯著的正向影響；負債比率與平均收帳期間均對整體技術效率有顯著的負面影響。研發費用率、經營年限與整體技術效率呈現正向關係，至於每人配備率則呈現負相關。 / The total output value of Taiwanese IC design industry is the globally second following United States. It had become the domestic gazed industry since mid 90s. Therefore, this research studies the production efficiency of global IC design industry and chooses the firms which sales globally ranked top 30 as samples. Through DEA, this thesis analyzes the efficiency focused on different regions and individual firms from 2000 to 2002. And finally, the Tobit regression model is proposed to find out the factors that influenced performances of firms and it could be reference for the domestic firms to improve their production efficiency and productivity. The results display: 1. By comparison with regions, the DEA results display that U.S.A. United States has the highest overall technical efficiency(TE) value during 2000 to 2002. Taiwan ranked behind U.S. both in 2000 and 2001 but dropped to the 3rd position in 2003. Observing performances of individual firms, Qualcomm, MediaTek, Marvell, ICS, Lattice, Pmc-Sierra and DSP Group performed as the best and their efficiency value is 1. 2. By comparison with regions, Malmquist Productivity Index(MPI) results display that the U.S.., Taiwan and Canada showed their Total Factors Productivity (TFP) to degenerate from 2000 to 2001. However, Taiwan and Canada showed their progressive TFP, progress in TFP but U.S.A. had a regressive TFP from 2001 to 2002. For one individual firm, there were only 4 firms’ TFP being aggressive from 2000 to 2001 and there are 15 firms’ TFP aggressive from 2001 to 2002. 3. The result from running Tobit regression models display that Inventory Turnover has significant positive effect to TE; Debate Ratio and Average Collection Period have significant negative effect to TE; R&D Ratio and Incorporated Period have direct relation with TFE and Equipment Per Employee has negative relation with TE.

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本研究欲了解有無集團企業背景對本國產險公司經營效率之影響，並透過本國有無集團企業背景產險公司之效率比較，求證關於代理理論之審慎管理假說及費用偏好假說是否成立。本研究運用資料包絡分析法(Data envelopment Analysis, DEA)及cross-frontier analysis評估民國81至90年，17家本國產險公司具集團背景及無集團背景各年度效率情況。另外，採用多元迴歸分析討論產險公司不同經營特性對經營效率的影響。本文亦利用Malmquist生產力指數分析具集團背景及無集團背景產險公司十年來生產力的變動。 　　本研究之實證研究結果如下： 一、於技術效率，具集團背景及無集團背景產險公司無技術效率差異，表示不論有無集團背景，皆有各自的經營技術優勢，符合審慎管理假說。於成本效率，發現81至85年時具集團背景產險公司之成本結構於生產無集團背景產險公司的產出相對較具成本效率，符合費用偏好假說；但86至90年時，則無集團背景產險公司之成本結構對於自己的產出較具成本效率，不符合費用偏好假說。 二、以多元迴歸分析於81至85年、86至90年，影響無集團背景產險公司效率之因素。本文發現於86至90年，當無集團背景產險公司總資產越小、再保比例越高，其以自己的成本結構進行生產，越顯著較以具集團背景產險公司的成本結構進行生產來得具成本效率。 三、二類產險公司於民國81至90年間生產力皆呈現衰退，且具集團背景產險公司衰退情況較嚴重。二類產險公司生產力衰退主因皆為生產技術的衰退；此外，具集團企業背景產險公司十年來的技術效率有衰退的趨勢，而無集團背景產險公司之技術效率則持續進步。若綜合前面成本效率的結果，則十年來具集團背景產險公司平均效率表現沒有進步跡象；而無集團背景產險公司有改善其效率。 / The purpose of this paper is to examine the effect of“group”background on the property-liability insurance industry in Taiwan. We test the managerial discretion hypothesis and the expense preference hypothesis by comparing the efficiency of the group and independent property-liability insurers. Using the property-liability insurance industry in Taiwan from 1992-2001 as our sample, we use DEA and cross-frontier analysis, and adapt multiple regression analysis to examine the variables which affect the efficiency performance in the property insurance company. We also measure the productivity changes of the group firms and independent firms over ten years. The empirical results are summarized as follow. First, we find that the technical efficiency results are consistent with the managerial discretion hypothesis, in that there is no difference in the technical efficiency between the group firms and independent firms. The results of cost efficiency between 1992 and 1996 show that the group frontier dominates the independent frontier for independent outputs, supporting the expense preference hypothesis. However, the results of cost efficiency between 1997 and 2001 no more support the expense preference hypothesis in that the independent frontier dominates the group frontier for independent outputs. Second, we find that the independent firms tend to have a comparative advantage over the group firms in the independent cost frontier when the independent firm’s size is smaller or when its reinsurance proportion is higher. Finally, the results of the Malmquist productivity analysis show that the productivity of group firms declines in 1992-2001, and the decay is due to their technical efficiency decreases. Conversely, the productivity of the independent firm improves.