University of Macau / Faculty of Education

University of Macau / Faculty of Education

University of Macau / Faculty of Education

在生態學與生物學上，物種數常作為生物多樣性的指標，以估計單一群體物種數為例，較知名的方法首推Good (1953)以在樣本中出現一次的物種為基礎，提出的物種數估計方法堪稱的先驅，隨後許多文獻延伸Good的想法，發展出許多的估計方法，例如Burham and Overton (1978)的摺刀估計法，Chao and Lee (1992)則以涵蓋機率方式估計。相對而言，兩群體的共有物種數的研究少有人探討，目前以Chao et al. (2000)的估計式較為知名。 本研究參考Good (1953)提出估計未發現物種出現機率的想法，估計未發現共有物種的機率，並以Burham and Overton (1978)中應用摺刀法估計物種數的概念，建立一階摺刀估計式與變異數，且另行以多項分配公式推導變異數估計式，進行電腦模擬與實際資料驗證並與Chao et al. (2000)提出的共有物種估計式比較。最後根據Rasmussen and Starr (1979)以抽樣成本建立最適停止規則的概念，應用於本研究所提出的估計式，並經由電腦模擬找出抽樣成本與物種分佈均勻程度的關聯，可作為設定停止規則的依據。 / The number of species is often used to measure the biodiversity of a population in ecology and biology. Good (1953) proposed a famous estimate for the number of species based on the probability of unseen species. Subsequently, many studies applied Good’s idea to create new estimation methods, For example, the Jackknife estimate by Burham and Overton (1978), and the estimate by using the sample coverage probability in Chao and Lee (1992) are two famous examples. However, not many studies focus on estimating the number of shared species of two populations, except the method by Chao et al. (2000). In this study, we modify Good’s idea and extend the Jackknife method of Burham and Overton (1978) to develop the estimate for the number of shared species of two populations. In addition, we also establish the variance formula of the estimator by using the multinomial distribution. Subsequently, we use computer simulation and real data sets to evaluate the proposed method, and compare them with the estimator by Chao et al. (2000). Finally, we adapt the idea of optimal stopping rule by Rasmussen and Starr (1979) and combine it with the proposed jackknife estimate. We found that using the sampling cost as the stopping rule is a feasible approach for estimating the number of shared species.

過去實證研究顯示，匯率轉嫁程度並非一成不變，而是具有隨時間變動的特性。因此，有別於過去文獻大多採用滾動相關係數，本文則是使用Engle(2002)提出的動態條件相關係數模型，估計台灣於1982年至2014年間匯率變動與進口價格變動間的動態條件相關係數；並以其做為匯率轉嫁的代理變數，進而探討台灣匯率轉嫁的時間變動趨勢。我們的實證結果顯示，不論是用滾動相關係數還是動態條件相關係數，台灣的匯率轉嫁都明顯具有隨時間變動的特性。雖然5年期與10年期的滾動相關係數均在1997年前後分別呈現上升與下降的趨勢，動態條件相關係數則無類似的現象。然而，由於滾動相關係數容易受到滾動視窗樣本大小或滾動視窗有無包含極端值的影響，使得此方法較無法看出匯率轉嫁變動的準確時間點，而動態條件相關係數模型則可避免此問題。此外，本文實證發現，通膨環境與匯率波動是造成台灣匯率轉嫁隨時間變動的主要因子，對匯率轉嫁皆有顯著的正向影響。在排除1986年匯率轉嫁與進口滲透率呈現短暫負向關係的資料後，進口滲透率與匯率轉嫁的正向關係變為顯著，而進口滲透率也成為影響匯率轉嫁的原因之一。 / According to past empirical studies, it is believed that exchange rate pass -through (ERPT) has the time-varying nature. In this paper, we apply the Dynamic Conditional Correlation (DCC) model of Engle (2002), rather than the rolling correlation coefficient prevalently used by other studies, to analyze the time trend of ERPT for Taiwan. We estimate the dynamic condition correlation between the changes of exchange rate and the changes of import price using monthly data from 1982 to 2014 and use this correlation as a proxy for the degree of ERPT. Our empirical results show that ERPT for Taiwan, whether measured by the DCC or the rolling correlation coefficient, has a significant time- varying nature. In addition, both 5-year and 10-year window rolling correlation coefficient increase before 1997 and decline after 1997, which does not show in the DCC. However, the rolling correlation coefficient does not provide precise timings in the changes in ERPT, because of the dependence on the size of windows and whether or not outliers exist in the window. In contrast, the DCC does not have this kind of problem. Another important empirical result of this paper is that the inflation environment and the exchange rate volatility are main factors which explain the time-varying ERPT, and both of them have positive relation with ERPT. Moreover, the import penetration becomes positively significant after excluding data which shows temporary negative impact of the import penetration on ERPT in 1986.

傳統參數隨機成本邊界模型需事先假設其函數型態，但真正的函數型態未知，若是假設錯誤的函數型態可能存在模型設定誤差，另外過去估計成本函數時，大多著重於技術效率的衡量，而忽略配置效率，如此一來，將導致模型參數估計產生偏誤，影響後來效率的計算。基於上述的問題，本研究將應用半參數隨機成本邊界模型且同時考量技術效率與配置效率，不但函數設定具有彈性且能正確的衡量效率值，然而在考量配置效率的衡量後，增加模型估計的困難度，使得估計收斂不易，因此本研究提出一個五階段的估計步驟，應用蒙地卡羅模擬進行分析，該估計步驟不但能簡化估計且能得到技術與配置效率的一致性估計。最後則將本研究提出的估計方法應用在實證研究上，探討14個東歐國家在轉型期間其技術與配置效率的衡量，使用不平衡縱橫資料，共340家商業銀行進行實證分析。 / Conventional parametric stochastic cost frontier models are likely to suffer from biased inferences due to misspecification and the ignorance of allocative efficiency (AE). To fill up the gap in the literature, this article proposes a semiparametric stochastic cost frontier with shadow input prices that combines a parametric portion with a nonparametric portion and that allows for the presence of both technical efficiency (TE) and AE. The introduction of AE and the nonparametric function into the cost function complicates substantially the estimation procedure. We develop a new estimation procedure that leads to consistent estimators and valid TE and AE measures, which are proved by conducting Monte Carlo simulations. An empirical study using unbalanced panel data on 340 commercial banks from 14 East European countries over the period 1993-2004 is performed to help shed some light on the usefulness of our procedure.

在生態學、生物學、和比較文學的研究中，物種個數通常是評估生物多樣性的重要指標，單一群落物種數的估計已有非常豐富的相關研究。較為知名者包括Good (1953)提出未出現物種的機率，作為估計物種數的參考，往後Good的想法被大量延伸，推演出不少新的估計方法，像是Burnham and Overton (1978)的Jackknife估計法，Chao and Lee (1992)利用涵蓋機率的估計。相對而言，兩群落共有物種數的研究較少，現有研究中較為知名的有Chao et al. (2000)的估計式。 本研究延伸Good想法，探討Jackknife估計法在兩群落的應用，以出現一次的共有物種（一階Jackknife估計），推估未出現共有物種機率，並且仿造Burnham and Overton的想法，建立共有物種數的估計值及變異數。本文除了以電腦模擬，也使用實例（包括：金庸武俠小說、台灣野生水鳥、巴拿馬螃蟹和巴洛科羅拉多森林）檢驗本文的Jackknife估計法，利用涵蓋機率角度發現抽出某特定比例樣本時，估計值涵蓋母體共有物種數之機率值達到九成以上，且也與Chao提出的估計值比較。 / The number of species is frequently used to measure the biodiversity of a population in ecology, biology, and comparative literature. There are quite a lot of studies related to estimating the number of species. Among these studies, Good (1953) proposed a famous estimate (Turing’s estimate) for the probability of unseen species. Subsequently, many methods have been proposed for estimating the number of species based on Good’s idea. For example, the Jackknife estimator by Burnham and Overton (1978) and sample coverage probability by Chao and Lee (1992) are two famous estimates for the number of species. In contrast, there are not many studies for the number of shared species in two communities, and Chao et al. (2000) is probably the only one. This article extends Good’s idea and the Jackknife method to estimate the number of shared species in two communities. Similar to Burnham and Overton, we establish the estimate and its estimated variance, based on the number of species appearing exactly once. We also use computer simulation and real data sets (Jin-Yong martial arts novels, Taiwan wild birds, Panama crustacean, and Barro Colorado Island forest) to evaluate the proposed method. We found that the coverage probability for confidence interval covering the true number of shared species is more than 90%. In addition, we compare the proposed method with Chao’s method.

In this paper, we propose two types of sieve bootstrap, univariate and multivariate approach, for the generalized method of moments estimators of time series data. Compared with the nonparametric block bootstrap, the sieve bootstrap is in essence parametric, which helps fitting data better when researchers have prior information about the time series properties of the variables of interested. Our Monte Carlo experiments show that the performances of these two types of sieve bootstrap are comparable to the performance of the block bootstrap. Furthermore, unlike the block bootstrap, which is sensitive to the choice of block length, these two types of sieve bootstrap are less sensitive to the choice of lag length.

隨著經濟發展與所得水準提升，臺灣地區就業人口由早期的第一級產業－農林漁牧業逐漸移向第二級產業－工業，再由第二級產業轉移至第三級產業－服務業。為瞭解臺灣地區服務業就業之趨勢，國內多數研究僅就蒐集資料以年齡、年代或世代三方面分別作探討，本文則改採流行病學領域中所廣泛使用之年齡－年代－世代模型（Age-Period-Cohort Model），就行政院主計處「人力資源調查」資料來作分析。但年齡、年代與世代三者間存在共線性問題（即世代＝年代－年齡），導致迴歸模型產生無限多組解，為了自其中選出一組較適當之參數估計值，文獻中提供了許多不同形式的解決方法。本文則採用Fu（2000）所提出之本質估計量（Intrinsic Estimator，簡稱IE），這是一種不受參數限制式影響的估計方式。我們除了藉以取得惟一的參數估計值，進而分析年齡、年代及世代效應對服務業就業比率之影響外，並與傳統之受限廣義線性模型估計量（Constrained Generalized Linear Models Estimator，簡稱CGLIME）作一比較，來說明採用本質估計量之優點及方便之處。 / Along with economical development and higher income level, Taiwan area employed population has gradually been switching from farming, forestry, fishing and animal husbandry to goods-producing industries, and then onto services-producing industries. In order to understand the trend of employment in service-producing industries in Taiwan, most domestic studies focus on the aspects of age, period or cohort separately. We, instead, adopt the Age-Period-Cohort (APC) model, which is well recognized in the epidemiology, to analyze the data from “Manpower Surveys” conducted by the Directorate-General of Budget, Accounting and Statistics, Executive Yuan, R.O.C. in this study. However, due to the collinearity among the age, period, and cohort effects, the APC model suffers from the identifiability problem. Some possible solutions have been provided in the literature. Among them, the Constrained Generalized Linear Models Estimator (CGIME) is undoubtedly the most popular choice, while the Intrinsic Estimator (IE) (Fu (2000)), which is invariant to the constraint selected to obtain the parameter estimates, is less well-known. We compare the results obtained from IE with that of CGIME in this study, and discuss the advantages of using the Intrinsic Estimator.

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.