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21	有限理性與彈性迷思 / Bounded Rationality and the Elasticity Puzzle 王仁甫, Wang,Jen Fu Unknown Date (has links) 在總體經濟學中，跨期替代分析方法佔有相當重要的地位。其中跨期替代彈性(the elasticity of intertemporal substitution, EIS)的大小，間接或者直接影響總體經濟中的許多層面，直覺上，例如跨期替代彈性越大，對個人而言，是對當期消費的機會成本提升，使延後消費的意願上升，同時增加個人儲蓄，在正常金融市場情況之下，個人儲蓄金額的增加，將使市場資金的供給量增多，使得企業或個人的投資機會成本降低，經由總體經濟中間接或直接的影響下，則總體經濟成長率應會上升。其中，當消費者效用函數為固定風險趨避係數(constant coefficient of relative risk aversion, CRRA)且具有跨期分割與可加性的特性，加上在傳統經濟學中，假設每個人皆為完全理性的前提下，經由跨期替代分析方法推導後，可以得到相對風險趨避係數(the coefficient of relative risk aversion, RRA)與跨期替代彈性(the elasticity of intertemporal substitution, EIS)恰好是倒數關係。 / 在過去相關研究中，Hansen and Singleton (1983)推估出跨期替代彈性值較大且顯著，但Hall (1988)強調，若考慮資料的時間加總問題(time aggregation problem)，則前者估計出跨期替代彈性在統計上則不再是顯著；Hall亦於結論提出跨期替代彈性為小於或等於0.1，甚至比0小。在經濟意義上，代表股票市場中投資人的相對風險趨避程度(RRA)極大，直覺上，是不合理的現象，這也是著名的彈性迷思(elasticity puzzle)。於是Epstein and Zin (1991)嘗試建議並修正效用函數為不具時間分割性(non-time separable utility)的效用函數，並得到跨期替代彈性(EIS)與相對風險趨避係數(RRA)互為倒數關係，不復存在的結論。這也說明影響彈性迷思(elasticity puzzle)的原因有許多，其中之一，可能為設定不同形式效用函數所造成。 / 在傳統經濟模型中，假設完全理性的個人決策行為之下，利用跨期替代方法，可以得到跨期替代彈性(EIS)與相對風險趨避程度(RRA)互為倒數關係後，又得到隱含風險趨避程度為無窮大的推估結論。這也是本研究想要來探究的問題，即是彈性迷思(elasticity puzzle)究竟是假設所造成，或者是因為由個體資料加總成總體資料，所產生的謬誤。 / 因此，本研究與其他研究不同之處，在於利用建構時間可分離形式的效用函數(time-separable utility)模型基礎，以遺傳演算(Genetic Algorithms)方法，建構有限理性的人工股票市場進行模擬，其中，模擬方式為設定不同代理人(agent)有不同程度的預測能力，代表其理性程度的差異的表現。 / 本研究發現在有限理性異質性個人的人工股票市場下，相對風險趨避程度係數(RRA)與跨期替代彈性(EIS)不為倒數關係，且設定不同代理人不同的預測能力，亦會影響跨期替代彈性(EIS)的推估數值大小。跨期替代彈性風險趨避係數基因演算法一般化動差法一般化最小平方法 RRA GMM GLS Genetic Algorithms
22	多期最適資產配置：一般化最小平方法之應用劉家銓 Unknown Date (has links) 本文主要是針對保險業及退休基金的資產負債管理議題為研究重心，延續Huang (2004)的研究，其研究是以理論求解的方式求出多期最適資產配置的唯一解，而其研究也衍生出兩個議題：首先是文中允許資產買賣空;再者其模型僅解決單期挹注資金的問題，而不考慮多期挹注資金。但這對於實際市場操作上會有一些的問題。因此本文延續了其研究，希望解決這兩個議題，讓模型更能解出一般化的資產負債管理問題。本文所選擇的投資的標的是以一般退休基金與保險業所採用，分別是短債(short-term bonds)、永續債卷(consols)、指數連結型債券(index-linked gilts(ILG))、股票(equity)為四種投資標的，以蒙地卡羅模型模擬出4000組Wilkie 投資模型(1995)下的四種標的年報酬率以及負債年成長率，利用這些預期的模擬值找出最適的投資比例以及應該挹注的金額。而本文主要將問題化為決策變數的二次函數，並以一般化最小平方法(generalized least square,GLS)來求出決策變數，而用此方法最大的優點在於一般化最小平方法具有唯一解，且在利用軟體求解的速度相當快，因此是非常有效率的。本文探討的問題可以分成兩個部分。我們首先討論「單期挹注資金」的問題，只考慮在期初挹注資金。接著我們考慮「多期挹注資金」的問題，是在計畫期間內能將資金分成多期投入。兩者都能將目標函數化為最小平方的形式，因此本文除了找出合理的資產配置以及解決多期挹注資金的問題之外，也將重點著重於找一個能快速且精準的方法來解決資產配置的問題。 / This paper deals with the insurance and pension asset liability management issue. Huang (2004) derives a theoretical close solution of multi-period asset allocation. However, there are two further problems in his paper. First, short selling is allowable. Second, multi-period investing is not acceptable. These two restrictions sometimes are big problems in practice. This paper extends his paper and releases these two restrictions. In other words, we intend to find a solution of multi-period asset allocation so that we can invest money and change proportion of investment in each period without problems of short selling. In this paper, we use the standard asset classes used by pension or insurance funds such as short-term bonds, consols, index-linked gilts and equities. We generate thousand times of Monte Caro simulations of Wilkie investment model (1995) to predict future asset returns. Furthermore, in order to improve time-efficiency and accuracy, we derive a quadratic objective function and obtain a unique solution using sequential quadratic programming. 資產負債管理一般化最小平方法多期最適投資 Asset liability matching generalized least square (GLS) multi-period approach
23	Contextualization of Autonomous Spaceflight Operations for deep space planetary encounters Marcinkowski, Michal January 2018 (has links) This work concerns the research and application of data visualization techniques to depict ongoing activities in mankinds investigation of space as part of a larger open-source visualization- and science-outreach software known as OpenSpace. It involves the construction of a physically accurate virtual environment of our local star group and solar system so as to facilitate development of a robust and generalized solution capable of articulating mission-science to its viewers. The research part is focused on deploying data visualization methods suitable for contextualizing scientific findings towards the general public in a pedagogical manner, with the end goal to provide a fully operational New Horizons visualization on the day of encounter with Pluto for the first public broadcast of OpenSpace across the globe. OpenSpace Visualization contextualization mission science American Museum of Natural History AMNH Hayden Planetarium Digital Universe Universe solar system Pluto Jupiter Encounter NASA New Horizons Data Visualization Rendering shader GLS Datateknik Datateknik Computer Engineering Datorteknik
24	FUNGICIDE TIMING, RESISTANCE MONITORING, AND PHYTOPATHOMETRY FOR FIELD CROP DISEASES IN INDIANA Kaitlin G Waibel (15353782) 26 April 2023 (has links) <p> </p> <p>Protecting crops from disease requires continuous research because plant pathogen incidence, geographical range, and pathogenicity, are constantly shifting variables as agronomic practices and climate continue to evolve. The objectives of this research are to i.) evaluate field-scale fungicide timing programs for corn (<em>Zea mays L.</em>) diseases at multiple locations in Indiana; ii.) evaluate field-scale fungicide timing programs for soybean (<em>Glycine Max</em> (L.) Merr.) diseases at multiple locations in Indiana; iii.) continue to identify, document, and confirm the distribution of populations of the soybean frogeye leaf spot pathogen (<em>Cercospora sojina)</em> that contain the G143A mutation conferring resistance to quinone outside inhibitor (QoI) fungicides in Indiana; and iv.) assess the incidence, severity, and prevalence of tar spot (<em>Phyllachora maydis</em>) in Indiana. For the first and second objectives, field scale trials were established at three locations in Indiana from 2019 to 2022. No application timings at any location provided significant yield protection for corn or soybeans. To achieve the third objective, 165 isolates of <em>C. Sojina </em>were tested. In total, 24 out of the 32 counties sampled in 2021 and 2022 were documented with QoI-resistance. The fourth objective was accomplished by surveying Indiana counties for incidence and severity of tar spot. As of 2022, 86 out of 92 Indiana counties have been confirmed for the presence of tar spot.</p> Agronomy Plant pathology fungicide timing Cercospora sojina Phyllachora maydis Tar spot of corn Frogeye leaf spot quinone outside inhibitor QoI resistance fungicide resistance detection Phytopathometry Gray leaf spot (GLS) corn soybean corn diseases soybean diseases
25	群集樣本具巢狀誤差結構之迴歸分析 / Regression analysis for cluster samples with nested-error structure 賴昭如 Unknown Date (has links) 分析具有巢狀誤差結構的迴歸模式時，惹忽略隨機誤差項之間的相關性，而採用最小平方(OLS)估計量所導出的標準 F 統計量（以 F<sup>S</sup>表之）進行檢定，會導致過大的型 I 錯誤機率；若將隨機誤差項之間的相關性納入考量，而採用廣義最小平方(GLS)估計量所導出的 F 統計量（以 F<sup>GLS</sup>表之），則計算上會較為繁雜。因此我們藉由轉換方式，將模式轉換成隨機誤差項之間彼此獨立的新模式後，再以 F<sup>S</sup> 進行檢定，其結果與直接以 F<sup>GLS</sup> 檢定相同，且可使計算較為方便。由於模式轉換所需的轉換矩陣為母體變異數的函數，因此當母體變異數未知時，我們以 Henderson 的常數配適（fitting-of-constants）方法來估計之。藉由模擬結果得知，若各段的觀察個數相等，則不論巢狀誤差結構為二段式(two-stage)或三段式(three-stage)，廣義最小平方估計量(GLS)均較最小平方估計量(OLS)表現穩定，且 F<sup>GLS</sup> 在檢定力及實際顯著水準方面的表現也都比 F<sup>S</sup> 好。 / When analyzing the regression model with nested-error structure, if the correlations between errors are ignored, and conduting the model adequacy test by the standard F statistic (F<sup>S</sup>) led from the ordinary leastsquares estimator (OLSE) , then the type I error rate will be inflated. However, if the corrlated structure is considered and the model is tested by F<sup>GLS</sup> led from the general least-squares estimator (GLSE) , the calculation will be more complicate. The model can be transformed to a new model with independent random errors and then, tested by F<sup>S</sup> . The result is the same as the one by F<sup>GLS</sup> , also it is more convenient for calculation. Since the transformation matrix is a function of variance components, we estimate variance components by Henderson's fitting-of-constants when they are unknown. Through simulation, it is concluded that if the observations in each stage of nested-error structure are the same, the GLSE is more stable than the OLSE in both two-stage and tree-stage structures. Also, the power and the sizes of F<sup>GLS</sup> will perform better than those of F<sup>S</sup> . 巢狀誤差 F檢定廣義最小平方法最小平方估計法常數配適實際顯著水準檢定力 Nested error F test GLS OLS Fitting-of-constants Sizes of the tests Power

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