本論文包括3篇時間序列方法在財務的應用。第一篇以門檻向量自我迴歸模型(threshold vector autoregression)分析股市訊息傳遞的不對稱效果；第二篇利用不對稱共整合模型(asymmetric cointegration)分析中國大陸股市之間長期均衡關係；第三篇根據追蹤資料共整合檢定(panel Cointegration test)檢定購買力平價說。 第一篇文章利用門檻向量自我迴歸模型分析Nasdaq股市對台灣、日本與韓國股市不對稱的訊息傳遞效果。實證結果發現，當Nasdaq市場處於下跌狀態時（壞消息狀態），Nasdaq市場干擾對亞洲股市的衝擊較大，反之，當Nasdaq市場處於上漲狀態時（好消息狀態）時，Nasdaq市場干擾對亞洲股市的衝擊較小，而在壞消息狀態時，Nasdaq指數大跌對Jasdaq指數與Kosdaq指數的衝擊效果大於Nasdaq指數大漲的效果，Nasdaq指數小跌所產生的衝擊與小漲所產生的效果具有對稱性。 第二篇文章以Enders and Siklos(2001)不對稱共整合模型探討，中國大陸上海及深圳A股與B股股價指數之間長期不對稱的均衡關係，實證結果發現，在1992年10月至2001年8月，上海A股指數與深圳A股指數之間具有不對稱共整合關係，且當上海A股處於好消息狀態（股市上漲）時，其誤差修正項的調整速度較壞消息狀態（股市下跌）之下為快，此外，上海A股指數與深圳A股指數之間其有雙向的連動關係。在B股開放之後，則是深圳股市A股與B股指數存在不對稱共整合關係，同時Granger因果關係檢定顯示深圳B股指數領先A股指數。 第三篇文章利用Pedroni(2001)追蹤資料共整合檢定，探討大麥克漢堡價格與CPI兩種不同的價格指數用於檢定購買力平價說的有效性，根據14個國家1992-1999年的追蹤資料得到的實證結果顯示，以名目匯率作為被解釋變數，則大麥克漢堡價格與CPI都是支持PPP假說，然而若以相對價格為被解釋變數，則只有大麥克漢堡價格是支持PPP假說，而以CPI為基礎的PPP假說則是無法得到支持。除此之外，本文的實證結論並不受生產力差異的影響。 關鍵字：門檻向量自我迴歸模型、不對稱共整合、追蹤資料共整合、股票市場、購買力平價說 / This dissertation includes three financial applications using time series methods. The first article investigates the asymmetric effects of information transmissions in stock markets using threshold vector autoregression model. The second article uses asymmetric cointegration to study the long-run equilibium relationships among Chinese stock markets. The third article uses panal cointegration to test purchasing-power parity (PPP). Firstly, we examines the asymmetric effects of information transmissions of Nasdaq stock market on Taiwan, Japan, and Korea stock markets by using a threshold vector autoregressive model. And also, we check whether Nasdaq stock market have different impacts on organized stock exchanges (including TAIEX, NIKKEI 225 Index, Korea Composite Index) and over-the-counter markets (including Taisdaq Index, Jasdaq Index, and Kosdaq Index) or not. The empirical results indicate that negative innovations in Nasdaq market (bad news regime) have large influence on Asia stock markets. Particularly, the positive innovations in Nasdaq market (good news regime) have small influence on Asia stock market. The large negative innovations in Nasdaq market have great influence than those of the large positive innovations on Jasdaq Index and Kosdaq Index in bad news regime. The second article uses Enders and Sikios's (2001) asymmetric cointegration model to investigate the long-run asymmetric equihbrium relationships. The empirical results find that there exits an asymmetric cointegrated relationship between Shanghai A share index and Shenzhen A share index for the period from October 1992 to August 2001. The adjustment parameters of error correction term at Shanghai A share market are larger in bad-news regime than those in good-news regime. This result reveals investors at Shanghai possess over-reaction behavior on news of stock market. Moreover, there exists a bi-directional Granger causality between Shanghai A share index and Shenzhen A share index. We find there exists an asymmetric cointegrated relationship between Shenzhen A share index and Shenzhen B share index after 19 February 2001. Furthermore, the Shenzhen B share index leads Shenzhen A share index after 19 February 2001. The third article uses Pedroni's (2001) panel cointegration test to examine the validity of PPP hypothesis by two different price indces, i.e. Big Mac prices and CPI. Our panel observations include 14 countries from 1992 to 1999. The empirical evidence indicates Big Mac PPP and CPI PPP is supposed if we use nominal exchange rate as the explanatory variable. Nevertheless, the Big Mac PPP is valid but CPI PPP not valid if we use price level as the explanatory variable. Moveover, our concludtion does not influenced by productivity bias. Keywords: threshold vector autoregression, asymmetric cointegration, panel cointegration, stock markets, purchasing-power parity

本論文目的在分析臺灣學生學習成就評量資料庫(TASA)之新移民子女學習成就、心理、背景特質表現，及與本土子女進行比較。研究中自描述新移民族群的歷史演變、發展、可能遭遇困境與相關的研究中，進而，再搭配現行既有評量架構，以決定針對國小(四、六年級)新移民子女之數學成就、性別、家庭結構、家庭語言環境、家庭社經地位、家人、自我教育期望、親子關係、學校適應、學習策略、學習偏好、能力歸因、數學能力自我概念、內在動機、家庭作業、課業補習、課外活動等十多項心理、背景特質進行分析，而在詮釋其理論、實徵研究基礎後，運用測量不變性、多元潛在迴歸分析等技術以進行本研究各項議題之探討，希冀能就分析結果，提供未來研究之參考。 綜整分析後，可發現以下幾點結論： 一、在針對數學成就進行族群間之比較時，研究中在單向度假設與Rasch模式適配度檢定符合下，進而於排除顯著差異試題功能測量指標後，是運用多元潛在迴歸分析以進行本土與新移民子女數學成就之比較，分析結果顯示，在國小六年級內，並未存在顯著的族群差異，但是於國小四年級中，則是以本土子女的表現平均優於新移民子女，其差異效果為0.36，約屬Cohen(1992)認為介於中等至小的效果量，而接續於分析是否存在其它因素影響時，分析結果是顯示在先天消極的族群因素外，族群間家庭社經地位的不均衡才是真正影響之主因。 二、有關族群間之心理與背景特質比較方面，研究中在經採用測量不變性以確立工具品質，並伴隨著卡方獨立性考驗、獨立樣本t檢定之運用後，其分析結果大致顯示出新移民子女是較本土子女，在家中手足數僅有1位、位居老大或獨子女、繼親家庭與身處雙語環境等現況具有相對的多數，但是在學生認知家人、自我教育期望、親子關係、學校適應、學習策略運用、學習偏好以至課業補習、課外活動時間分派等，整體而言，則多是未具顯著的差異或屬Cohen(1992)所認為小的差異效果量範圍內。另一方面，研究中更突顯出的是族群間家庭社經地位的不平衡，確實占有極大的比重，是具備Cohen認為之中等至小的差異效果。 三、在分析心理、背景特質與數學成就關聯時，研究中在經採多元潛在迴歸分析後，其結果大致顯示：就族群與變項間之交互作用而言，國小四年級學生所認知之自我教育期望、內在動機等，相較於新移民子女，是對本土子女之數學成就有較高的影響力，但是國小六年級學生的家中手足數、家庭社經地位、記憶、控制策略運用與外控能力歸因的認知上，則是相對在新移民子女之數學成就上有較深厚的影響，而至於其它變項之分析，則多未存在族群的差異。進而，在排除族群的干擾，僅就各特質與數學成就關聯之主要效果而言，研究結果顯示除性別、家庭子女的出生序、家庭作業與課業補習分派時間等，存在不顯著或僅部份年級具備顯著效果外，其餘特質皆能符合本研究理論所預測之傾向，大致是以具備過多家中手足數或持過度的外控數學能力歸因者，是會對學生數學成就產生負面的影響，而其餘如來自完整家庭、身處主流語言環境、具備高家庭社經地位、教育期望、和諧親子關係、良好學校適應與擁有高度學習策略、偏好、動機等，則皆能正向的影響數學成就。 最後，研究中是就新移民子女定義、抽樣架構、測量指標等面向，提出對新移民子女學習成長資料庫之建立及與其它實務、研究之建議。 / The purpose of this study is to analyze neo-immigrant children’s achievement, performance in psychological traits and compare these with native children. Starting from the description of development, challenge and related researches of immigrant children in the past, we found the assessment frame of Taiwan Assessment of Student Achievement (TASA) just corresponded to the purpose we expect. Therefore, based on TASA , we decided to analyze the status and performance of neo-immigrant children in math, gender, family structure, language environment, socio-economic status, parents and self expectation, parents-children relationship, school adjustment, learning strategy, learning preference, ability attribution, academic self-concept, intrinsic motivation, time assignment of homework, school cramming, and extracurricular activity etc. Following the support of the theoretical and empirical evidences, we used the technique of measurement invariance and multiple latent regression to discover the different topics in the thesis. In sum, some conclusions are proposed. 1. In the comparison of math achievement between neo-immigrant and native children, we use the technique of multiple latent regression to test the difference after exclusion some items with significant differential item functioning. The result indicates that there is no significant difference between these two races in the sixth grade, but in the fourth grade the native children perform better than neo-immigrant children. The effect size is 0.36, and it is between medium and small degree according to Cohen(1992)’s opinion. Depended on this result, several following analyses are implemented. It shows the inequality of family socio-economic status between races is the main cause instead of the race people belong to. 2. In the comparison of psychological traits between neo-immigrant and native children, we not only use the technique of measurement invariance to ensure the qualities of assessment tools but also adopt chi-square test and independent samples t-test to examine the differences. The results indicate that the neo-immigrant children have more chances belonging to the categories of being eldest among siblings or only one child in his family, living in step-parents family, and involvement in bilingual environment. But there is almost no or just little difference between the two races in the perception of parent and self expectation, parents-children relationship, school adjustment, learning strategy application, learning preference, and the time assignment of school cramming and extracurricular activity. Besides, we found the inequality of family socio-economic status between races is the most different. The effect sizes range approximately from small to medium degree according to Cohen(1992)’s opinion. 3. In this study, we used the technique of multiple latent regression to analyze the relationship between different psychological traits and math achievement. The results indicate that as far as the interaction effects are concerned, the perception of self expectation and intrinsic motivation has greater impacts on native children’s math achievement than neo-immigrant’s in the fourth grade, but in the sixth grade the family size, family socio-economic status, the usage of memorization and control strategy, external ability attribution have greater impacts on neo-immigrant children’s math achievement than native children’s. Besides, there are no interaction effects on the remaining variables. Furthermore, as far as the main effect is concerned, the result shows that except no or just little relationship between gender, birth order, the time assignment of homework, school cramming and math achievement, the remaining variables have the similar relationships toward math achievement as we expect. That is that having too many siblings, external attribution of math ability may have negative influence on students’ achievement, but students who belong to intact family, involve in the mainstream of language environment, have higher family socio-economic status, and perceive better parent and self expectation, parents-children relationship, school adjustment etc. may have positive influence on their math achievement. Finally, some suggestions about the definition of neo-immigrant children, the way of sampling and qualities of measurement indicators are proposed. We expect they may do a greater help in the construct of the database of Prospect of Neo-Immigrant Children Learning and future usage.

本研究旨在利用風險管控的方式，來協助電子製造業建立一套可有效的維持產品的綠色品質並降低產品的綠色風險的綠色品質風險管控模型，使得企業不致因產品在出貨後，被檢測出違反RoHS指令而使企業被罰以巨額款項並損失商譽。 回顧1997年12月聯合國氣候變化框架公約(UNFCCC)參加國第三次會議在日本京都舉行，並簽定了[京都議定書]之後，各國陸續制定出其各自的環保法令，其中又以歐盟於2003年2月通過並於2006年7月1日起實施限制鉛，鎘，汞，六價鉻，多溴聯苯，多溴聯苯醚等六項有害物質的RoHS指令的影響範圍最大且最為直接的影響到我國的產業，從而引發起了本研究的動機。 本研究透過與訪談個案的合作，實際從分析個案的產品研發生產的作業中，由影響RoHS的角度從作業一直剖析到管控內容，進而找到會影響RoHS品質不良的16個風險因子，並透過建立的監控系統來進行風險因子的資料採樣，最後經由羅吉斯迴歸模型，建立出一套風險計算模型，以連接RoHS風險因子的監控系統而成為一套綠色品質風險管控模型。 / The objective of this research is to help electronic manufacturers to establish a Green Quality Risk Management Model, which can effectively keep green quality and decrease green quality risk of products. Consequently, companies can prevent huge amount of fine and goodwill impairment caused by RoHS violation of their shipments. After the participants of UNFCCC held the third meeting in Kyoto, Japan and ratified the Kyoto Protocol in December 1997, every country created its environmental regulations in secession. Among those regulations, the RoHS directive, which prohibits the usage of Lead, Mercury, Cadmium, Hexavalent chromium (Cr6+), Polybrominated biphenyls (PBB)and Polybrominated diphenyl ether (PBDE), adopted in February 2003 and activated in January 2006 by the European Union resulted in most pervasive and direct impact on Taiwanese industry, consequently creating the incentive for this research. By the cooperation of case interview, this research analyze the research and development operations of interviewees with the perspectives from primary operations to floor control in order to identify sixteen risk factors of RoHS quality, and sample the data of risk factors with established control system. Finally, a green quality risk management model was created by the establishment of a risk computation model in connection with RoHS risk factor control system was established using Logistic Regression model.

本論文主要在估計門檻式自動迴歸模型之參數的信賴區間。由線性自動迴歸 模型衍生出來的非線性自動迴歸模型中，門檻式自動迴歸模型是其中一種經常會被應用到的模型。雖然，門檻式自動迴歸模型之參數的漸近理論已經發展了許多；但是，相較於大樣本理論，有限樣本下參數的性質討論則較少。對於有限樣本的研究，Woodroofe (1989) 提出一種近似法：非常弱近似法。 Woodroofe 和 Coad (1997) 則利用此方法去架構一適性化線性模型之參數的修正信賴區間。Weng 和 Woodroofe (2006) 則將此近似法應用於線性自動迴歸模型。這個方法的應用始於定義一近似樞紐量，接著利用此方法找出近似樞紐量的近似期望值及近似變異數，並對此近似樞紐量標準化，則標準化後的樞紐量將近似於標準常態分配，因此得以架構參數的修正信賴區間。而在線性自動迴歸模型下，利用非常弱展開所導出的近似期望值及近似變異數僅會與一階動差及二階動差的微分有關。因此，本論文的研究目的就是在樣本數為適當的情況下，將線性自動迴歸模型的結果運用於門檻式自動迴歸模型。由於大部分門檻式自動迴歸模型的動差並無明確之形式；因此，本研究採用蒙地卡羅法及插分法去近似其動差及微分。最後，以第一階門檻式自動迴歸模型去配適美國的國內生產總值資料。 / Threshold autoregressive (TAR) models are popular nonlinear extension of the linear autoregressive (AR) models. Though many have developed the asymptotic theory for parameter estimates in the TAR models, there have been less studies about the finite sample properties. Woodroofe (1989) and Woodroofe and Coad (1997) developed a very weak approximation and used it to construct corrected confidence sets for parameters in an adaptive linear model. This approximation was further developed by Woodroofe and Coad (1999) and Weng and Woodroofe (2006), who derived the corrected confidence sets for parameters in the AR(p) models and other adaptive models. This approach starts with an approximate pivot, and employs the very weak expansions to determine the mean and variance corrections of the pivot. Then, the renormalized pivot is used to form corrected confidence sets. The correction terms have simple forms, and for AR(p) models it involves only the first two moments of the process and the derivatives of these moments. However, for TAR models the analytic forms for moments are known only in some cases when the autoregression function has special structures. The goal of this research is to extend the very weak method to the TAR models to form corrected confidence sets when sample size is moderate. We propose using the difference quotient method and Monte Carlo simulations to approximate the derivatives. Some simulation studies are provided to assess the accuracy of the method. Then, we apply the approach to a real U.S. GDP data.

本研究之宗旨在於探究中國大陸對亞洲區域內國家的金融市場影響程度之變化。由過去的各國股市日報酬率資料間相關程度與政策改變間的影響結果，來觀察是否未來在兩岸政策更開放下會使中國大陸對台灣的影響程度上升，進而使國際間投資組合的風險分散效果下降。本研究自DataStream選取台灣、香港、中國大陸、泰國、印尼、新加坡、馬來西亞、菲律賓、日本以及美國等十國的股價指數日資料，以對數轉換為日報酬率後年化加以分析。選取時間自1991年7月15日(中國大陸上海證券交易所股價指數公開後)至2008年12月31日。本研究選用的方法為使用風險值(VaR; Value at Risk)的概念來取代傳統的標準差，衡量以該十國所分別組成的各投資組合風險值變動情形；以及由風險值所衍生出的Diversification Benefit與Incremental VaR的結果。發現到僅由亞洲區域國家內組成的投資組合風險分散效果逐漸下降；且效果並不如有納入區域外國家(如美國)的投資組合。接著本研究將Gaussian Copula模型放入VaR中以增加對極端值的捕捉能力，結果發現本研究所選用的指數加權移動平均法所求得之相關係數已可有效反應出各國之間的相依程度，即加入Copula的效果有限。另外藉由Copula所求得之相關係數顯示，台灣、香港對中國大陸之間的相依程度已逐漸上升，並開始出現超越美國之現象，其中又以2005年為上升趨勢的起點。最後本研究以向量自我迴歸模型(VARs)來驗證2005年前後中國大陸股市對其他亞洲區域國家的影響力是否存在結構性的改變；並再佐以變異數拆解之方法來觀察2005年前後各國家之間自發性衝擊對彼此之間的影響程度變化。研究結果發現，透過VARs可證明中國大陸對亞洲區域各國的影響力在2005年後轉變為顯著；僅對美國不存在此一現象。另外變異數拆解的結果也顯示各國之間的相依程度在2005年後有明顯的上升，中國大陸對各國的影響程度亦然。透過本研究之結論，在未來兩岸將簽訂金融監理備忘錄使整合關係提升的環境下，需提醒投資人整合關係的上升將使得以之為標的之投資組合風險分散效果下降，需作為投資策略之考量。 / The object of this research is to find out the trend of dependence and correlation between China and other Asian countries. Based on past information about the relationship between equity markets’ correlation and changes in policies, this research can make suggestions to the foreseeable future of Taiwan and China whose relationship will be more solid due to new policy. The data of this research are gathered from DataStream, which includes Taiwan, Hong Kong, China, Thailand, Indonesia, Singapore, Malaysia, Philippines, Japan and United States. Selected from 1991/07/15 (when the Shanghai SE Composite went public) to 2008/12/31, this research calculates the annualized daily return using natural logarithms of two consecutive daily index prices. This research uses Value at Risk (VaR) to measure the risk exposure of portfolios formed by ten countries, and extends to the use of Diversification Benefit and Incremental VaR. The results found out that the diversification effects of portfolio which includes only Asian countries are decreasing and inferior to the effects when cross region countries are included. The second study of this research is to combine Gaussian Copula Model with VaR to capture the effects of extreme values. Empirical results found out that the VaR using Exponentially Weighted Moving Average method is good enough for analyzing Asian stock markets. The correlation in Copula model suggests that the dependence between Taiwan and China had increased since 2005 and has the increasing trend which might overwhelm the dependence between Taiwan and United States. Final research is about using Vector Autoregressions Model (VARs) to testify is there exist any structural change of dependence before and after 2005, and using Variance Decomposition to observe the relationships between these ten countries. The results found out that there exist structural change in 2005, the post-2005 periods shows that for Asian countries the effect from China are significant and greater than pre-2005 periods.

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.

本文主要在探討自變數存在有測量誤差時，邏輯式迴歸模型的估計問題，並設計實驗使得測量誤差能滿足遞減假設，進一步應用序貫分析方法，在給定水準下，建立一個信賴範圍。 當自變數存在有測量誤差時，通常會得到有偏誤的估計量，進而在做決策時會得到與無測量誤差所做出的決策不同。在本文中提出了一個遞減的測量誤差，使得滿足這樣的假設，可以證明估計量的強收斂，並證明與無測量誤差所得到的估計量相同的近似分配。相較於先前的假設，特別是證明大樣本的性質，新增加的樣本會有更小的測量誤差是更加合理的假設。我們同時設計了一個實驗來滿足所提出遞減誤差的條件，並利用序貫設計得到一個更省時也節省成本的處理方法。 一般的case-control實驗，自變數也會出現測量誤差，我們也證明了斜率估計量的強收斂與近似分配的性質，並提出一個二階段抽樣方法，計算出所需的樣本數及建立信賴區間。 / In this thesis, we focus on the estimate of unknown parameters, experimental designs and sequential methods in both prospective and retrospective logistic regression models when there are covariates measured with errors. The imprecise measurement of exposure happens very often in practice, for example, in retrospective epidemiology studies, that may due to either the difficulty or the cost of measuring. It is known that the imprecisely measured variables can result in biased coefficients estimation in a regression model and therefore, it may lead to an incorrect inference. Thus, it is an important issue if the effects of the variables are of primary interest. When considering a prospective logistic regression model, we derive asymptotic results for the estimators of the regression parameters when there are mismeasured covariates. If the measurement error satisfies certain assumptions, we show that the estimators follow the normal distribution with zero mean, asymptotically unbiased and asymptotically normally distributed. Contrary to the traditional assumption on measurement error, which is mainly used for proving large sample properties, we assume that the measurement error decays gradually at a certain rate as there is a new observation added to the model. This kind of assumption can be fulfilled when the usual replicate observation method is used to dilute the magnitude of measurement errors, and therefore, is also more useful in practical viewpoint. Moreover, the independence of measurement error and covariate is not required in our theorems. An experimental design with measurement error satisfying the required degenerating rate is introduced. In addition, this assumption allows us to employ sequential sampling, which is popular in clinical trials, to such a measurement error logistic regression model. It is clear that the sequential method cannot be applied based on the assumption that the measurement errors decay uniformly as sample size increasing as in the most of the literature. Therefore, a sequential estimation procedure based on MLEs and such moment conditions is proposed and can be shown to be asymptotical consistent and efficient. Case-control studies are broadly used in clinical trials and epidemiological studies. It can be showed that the odds ratio can be consistently estimated with some exposure variables based on logistic models (see Prentice and Pyke (1979)). The two-stage case-control sampling scheme is employed for a confidence region of slope coefficient beta. A necessary sample size is calculated by a given pre-determined level. Furthermore, we consider the measurement error in the covariates of a case-control retrospective logistic regression model. We also derive some asymptotic results of the maximum likelihood estimators (MLEs) of the regression coefficients under some moment conditions on measurement errors. Under such kinds of moment conditions of measurement errors, the MLEs can be shown to be strongly consistent, asymptotically unbiased and asymptotically normally distributed. Some simulation results of the proposed two-stage procedures are obtained. We also give some numerical studies and real data to verify the theoretical results in different measurement error scenarios.

隨著知識經濟時代以及全球化的來臨，各國都市面臨全球性的競爭壓力，都市內部業種轉變為以服務業為業種主流，其中的生產者服務業更是成長快速的新興產業，更是適應全球化發展策略的重要產業。因此了解其聚集的成因將更具有時代的意義。產業聚集於都市的現象，傳統聚集經濟多以規模經濟、地方化經濟、都市化經濟為其解釋。近來，由於知識經濟的重要性，衍生出以知識的創新效果以及外溢效果來解釋產業的聚集現象。本研究利用空間距離的影響性來估計產業內知識外溢效果，並利用產業投入產出關聯係數估計產業間知識外溢效果，並採用分量迴歸來進行實證分析，以利於更詳實的解釋各知識外溢效果在於不同公司規模下的影響性。 研究結果發現，投資於R&D要素對於公司的成長並沒有顯著的影響，甚至對於中小型公司為負向影響。產業內知識外溢效果方面，對於各公司規模皆有正向的幫助。產業間知識外溢效果則呈現對大規模公司有正向幫助，對中小規模公司則為負向效果。在知識經濟時代下，兩種知識外溢效果確實有助於大規模公司的成長，為產業聚集的重要成因之ㄧ，但對於中小規模公司則有不同的效果產生。因此在促進產業發展的區位規劃設計上，需要考量不同規模公司與不同知識外溢效果的相互關係，以便有助於生產者服務業的發展，建立更具有國際競爭力的都市型態。 / Facing the developing economics of knowledge and globalization, Taipei metropolitan area has faced the issue of industrial restructuring. The strategy for globalization is to develop the producer services (PS) sector, in order to improve higher competitiveness to the city. The theory of agglomeration economics includes scale economics, localization economics and urbanization economics. Nowadays, spillover effect is considered as the most important reason that industries agglomeration develop in urban areas. This paper analyzes two categories of R&D spillover effect in the PS sector of Taipei metropolitan area by quantile regression. The spatial distance and the Input-Output table are used to calculate the intra-industrial and inter-industrial spillover effect, respectively. The result shows that investment in R&D has a negative effect to firm’s output. Firms with any scale benefit from the intra-industrial spillover effect incomplete. The inter-industrial spillover effect analysis shows that only the bigger scale firms can get the positive benefit, the smaller scale firms get the negative effect. The research analyzes different kinds of R&D spillover effect, and the result show that there are different effects among different conditions. In sum, this research suggests that the disposition of industrial location should consider both the scale of firm and the different spillover effect. As above, utilizing the R&D spillover effect is an important factor to develop the PS sector and to construct the global city.

近年來，金融資產報酬波動的推估一直是重要的研究課題。然而，過去的波動不對稱研究均集中在股票市場，探討外匯市場波動不對稱性的實證研究並不多，但若忽略其不對稱效果將影響未來波動預測的正確性。因此，本研究利用近十六年來美元及日圓兌台幣匯率日資料，以傳統的波動不對稱性指數型GARCH模型(EGARCH Model)、門檻型GARCH模型(TGARCH, GJR GARCH Model)，亦延用異質自我相關迴歸模型(HAR-RV Model)及修正型異質自我相關迴歸模型(Modified HAR-RV Model)分別探討美元及日圓兌台幣匯率波動是否存在不對稱現象及其不對稱程度，並加以分析。實證研究中，上述四種模型均顯示美元及日圓兌台幣匯率波動的確具有不對稱效果；美元兌台幣匯率波動，與股票市場一致，報酬率與波動度間呈負向關係，當台幣相對美元升值時，波動度較高；而日圓兌台幣匯率波動，與美元匯率變動方向相反，報酬率與波動度間呈正向關係，當台幣相對日圓貶值時，波動度較高。此外，以異質自我相關迴歸模型實證分析中，日波動落後項的影響力明顯大於週、月、季波動落後項，與Muller, et al. (1997)、Corsi (2004)及Andersen, et al. (2005)實證研究結果類似。

隨著國際化的發展，企業為降低財務報表重編的相關成本，採用全球統一的財務會計準則已成趨勢。近年來，我國之財務會計準則委員會陸續修訂了若干公報，其中之一為第十號公報。該公報修訂至生效時期，正值全球金融風暴。因此，政府對實施該公報的時機決策反覆，而企業界、會計界及媒體皆對該公報相當關注。 本研究旨在探討新十號公報對整體市場、各產業及不同特性公司之影響。本研究採用事件研究法，以似乎不相關迴歸模式，分析我國公司在該公報修訂期間及政府實施決策期間之股票累積平均異常報酬率。 研究結果指出，在新十號公報修訂期間，市場對該公報的反應較為負面；在政府政策反覆期間，市場反應則有正有負。各產業部份，電子業於該公報修訂初期受影響較大，推論原因為電子業固定成本高、毛利較低之緣故；而在該公報政府實施決策階段則受影響較小，推論原因為大部分電子業公司均做好因應措施。公司特性部分，大公司對該公報愈持正面態度；老公司則較排斥該公報；前三年度虧損的公司，受該公報影響較小；而成長機會較多之公司，在該公報制定初期受影響較大，但於政府實施決策階段反而受影響較小。 / To pursue globalization, it has been a trend for firms to gradually adopt the International Accounting Standards; that would help reduce the costs related to cross-border restatement of financial reports. To converge with the International Accounting Standards, the Financial Accounting Standards of Taiwan has been revised in recent years. One of the revised standards is the new SFAS No. 10. That revision took place amid the financial crisis. Before the new standard became effective, the government in Taiwan swung on the timing issue of formal adoption. As a result, firms, practitioners, and the media all paid close attention to the new SFAS No. 10. This study aims to examine the revision and adoption decision effects of the new SFAS No. 10 on the stock market, industries, and firms. Firm characteristics that could be factors of the reaction were also examined. This research is an event study using seemingly unrelated regression to analyze the accumulated average abnormal returns of stocks of listed companies in Taiwan. The empirical results are as follows. First, the whole market mostly reacted negatively in the standard revision period and in either way during the standard adoption decision period. Next, in the standard revision period, the electronic industry also reacted negatively, as it would be more affected because of its higher fixed cost and lower gross profit. Yet, in the adoption decision period, the electronic industry was less affected likely because most electronics firms were ready for adoption. Thirdly, as to firm characteristics, bigger firms were willing to adopt the new standard, but older firms were not so. In addition, firms that had losses in the previous three years were less affected by the new standard. Finally, firms that had greater growth opportunities were more affected in the early period of standard revision but became less affected in the adoption decision period.