none

No description available.

No description available.

內生經濟成長認為政府支出結構與收入結構均會影響恆定狀態成長率之觀點和事實是否一致呢？許多先前相關之研究需被修正，因為這些研究在政府預算限制式的設定並不完全，導致實證之結果產生偏誤。本文除了將討論忽略這些偏誤所會導致之影響外，進一步將以One-Way與TwO-Way的固定效果與隨機效果模型來進行估計，並修正先前研究所發生之問題，然後找出強力的證據來支持Barro模型（1990）所做的預測。本文的樣本資料取自東亞經濟前瞻與亞洲開發銀行，以亞洲新、舊四小龍，加上申國，共九個國家的17年時間序列資料所形成的揉合資料（pooling data）為實證對象。分析結果發現在考慮異質性與同期相關性下，若採用似無相關迴歸技巧來對One-Way固定效果模型進行估計，則：（1）不論政府是以租稅收入或是非租稅收入作為融通財源，資本性支出的增加均會提高經濟成長率，經常性支出的增加則均會對經濟成長率造成負面影響；（2）在政府以租稅收入為融通來源下，非租稅收入增加將會降低經濟成長率；若政府以非租稅收入作為融通之財源，則此時租稅收入之增加將會降低經濟成長。 〔關鍵字〕資本支出、經常支出、同期相關性（Contemporaneous correlation）、異質性（HetereSkedaStiCity）、One-Way fixed（random）effect model、Two-way fixed（random）effect model、似無相關迴歸（Seemingly unrelated regression）

長期性資料(longitudinal data)，是指對同一個觀察個體(subject)或實驗單位(experiment unit)，在不同時間點上重複觀察或測量一個或多個變數。雖然觀察個體之間互相獨立，但就同一個個體而言，不同時間的觀察或測量常常是有相關性的。且觀察的個體之間可能由於一些無法測量的環境因素造成個體之間有差異，因此在傳統橫斷面分析中，假設其有相同迴歸係數的邊際模型可能不合理。隨機效果模型可以解決長期資料分析的相關，並假設每個個體的迴歸係數不同；此模型不但可以說明橫斷面資料的cohort效果，也可直接解釋長期資料的age效果；更可以區分個體之間與個體之內的變異。 本研究以1995年至2000年台灣11個產業中的100家公司之每股盈餘與各財務比率，作為實證分析的資料；分別配適每股盈餘與時間、產業別、時間產業別交互作用及財務比率及排除每股盈餘有異常值後之邊際效果模型(一般迴歸分析)及隨機效果模型，並比較其參數估計之異同。實證結果顯示，一般迴歸分析與假設誤差不相關且等變異下的隨機效果模型參數估計相似，但後者能區分變異為個體之間(between-subjects)與個體之內(within-subject)的變異。而假設誤差不相關且不等變異與假設誤差服從AR(1)且不等變異下的隨機效果模型估計相近。實證結果並顯示，在排除異常值後的模型參數估計，一般迴歸分析不論是估計值及顯著性大多沒有很大差別；而隨機效果模型的估計在排除異常值前後較有差別。特別是現金流量比率(CFR)原本為不顯著變數，在排除異常值後的模型配適全部變顯著性變數。 / The defining characteristic of a longitudinal study is that individuals are measured repeatedly through time. Although it is independent between subjects, the set of observations on one subject tends to be inter-correlated. Because there is some natural heterogeneity due to unmeasured factors between subjects, it is not corrected to assume they have the same regression coefficients. A random effect model is a reasonable description about the different regression coefficients, and it can resolve the inter-correlation of the observations on one subject. The major advantages of the random effect model are its capacity to separate what in the context of population studies are called cohort and age effects, and it can distinguish the variations between subjects and within subjects. This study describes the marginal model and random effect model, and shows their difference by real data analysis. We apply these models to the earnings per share (EPS) and other financial ratios of one hundred companies in Taiwan, which are distributed in eleven industries. The results show that the parameter estimates of the marginal model and random effect model are similar when error structure is independent and of equal variance. Furthermore, the latter can distinguish the variations between subjects and within subjects. However, the residual analysis reveals that the error structure may not be constant. Therefore, we consider heteroscedasticity error in random effect model. We also assume that error follows an autoregressive process (e.g. AR(1) model), which leads to the optimum among our results in terms of residual analysis. There are some observations that appear to be outlying from the majority of data. The results show little difference in the marginal models no matter whether those outliers are included. However, we obtain different results in the random effect models. Especially, the variable of “cash flow ratio” becomes significant once those potential outliers have been excluded, while it is not significant when all cases are fitted in the model.

Closed-end funds’ (CEF) discount and discount fluctuations have been puzzling researchers for decades. Up to date, there are no multidimensional or cross-sectional variables that have been proved to influence CEFs simultaneously. Fact is that, earlier research and theories on the subject are contradictious and several suggestions on the origin of the CEF’s discount and its fluctuations have been proposed. To mention a few, investor sentiments, taxation issues, dividend policies, agency costs and agency problems are considered to influence these discounts. The purpose of this report is to examine the relationship between micro- and macroeconomic variables fluctuations, and how these can explain the discount fluctuation of the Swedish CEFs. This report focuses upon the CEFs traded at NASDAQ OMX Stockholm, which have been selected through a comprehensive multistage selection process. 10 CEFs were selected. Monthly data for calculating micro- and macroeconomic variables was collected for the period March 2003 – February 2013, which resulted in approximately 1 200 observations. OLS regression analysis, Fixed- and Random Effect Models and Hausman tests were conducted. The findings conclude that some of this report’s chosen micro- and macro variables influence on the Swedish CEFs’ discount fluctuation, although these findings are conditioned. The CEFs’ individual characteristics or traits result in a significant impact on the fluctuation of CEFs’ discount. Hence, only by controlling these characteristics, multidimensional or cross-sectional micro- and macroeconomic variables can be proved to affect the CEFs’ discount fluctuation.

The hospitality industry has experienced significant changes in its competitive environment over the past 30 years, driven by the growth of alternative accommodations, the widespread use of the internet for searching and booking accommodations, and the adoption of asset-light business models. In this new competitive landscape, hospitality firms struggle to gain a competitive advantage, particularly as they lack rare and inimitable resources, which are considered crucial for achieving competitive advantage according to resource-based view literature. This dissertation explores three sets of strategies that enable hotel firms to attain a competitive edge despite their resources being non-rare and easily imitated by competitors. The first essay examines the potential for hotel firms to benefit from competitors' resources by co-locating with them. Although this strategy has been widely studied in organizational research, recent developments in the competitive market, such as internet adoption and the growth of short-term leases, have not been considered. Evidence suggests that internet adoption decreases the likelihood of low-level hotels entering markets with high-level hotels and negatively moderates the positive effect of branded hotels on independent hotels' performance, as well as nullifying the effect of low-level hotels on high-level hotels' performance. Additionally, short-term leases impact hotels' decisions and performance, as hotels tend to avoid co-locating with short-term leases with similar price points, and short-term leases can appropriate positive agglomeration externalities created by high-level hotels. The second essay investigates whether hotels can outperform competitors by gaining an advantage in resource appropriation through entering the market with a dual-branded hotel. Results indicate that a competitive advantage is achieved when at least one brand in the composition possesses better resources than competitors. Lastly, the third essay concentrates on the potential for hotels to leverage tacit knowledge transmission to increase the difficulty for competitors to imitate them. Findings reveal that the closer a hotel or short-term lease is to the nearest accommodation managed by the same hotel management company or host, the higher the chances of achieving a competitive advantage. Moreover, short-term leases can base their competitive advantage on idiosyncratic knowledge transferred from the platform, and they can compete in size with incumbent hotels if they have a high concentration of ownership in the market. / Doctor of Philosophy / The lodging industry has undergone numerous changes in the past 30 years, with the widespread adoption of the internet, the growth of the short-term lease market, and the implementation of asset-light strategies significantly impacting how hotels compete locally. This dissertation examines various market-entry strategies that can enable hotel firms to achieve a competitive advantage in local markets. The first essay explores the advantages of co-locating with competitors. Results indicate that previously identified benefits, such as reduced search costs for customers leading to higher performance for clustered competitors, have been diminished or nullified by the extensive use of the internet for searching and booking hotels. Independent hotels may still gain agglomeration advantages by co-locating with branded hotels, but the benefits are substantially reduced due to internet usage. Furthermore, the presence of different levels of short-term leases in the market affects hotels' entry patterns, which tend to diverge from short-term leases. Additionally, low-level short-term leases tend to capture agglomeration benefits created by high-level hotels, resulting in decreased performance for low-level hotels. The second essay investigates the optimal strategy for entering the market with a dual-branded hotel. Results show that, overall, adopting a vertically diversified strategy (i.e., where one of the two brands in the composition is of a higher class compared to the other) is preferable, with the higher class above the market's average class and the lower class below it. Conversely, the least effective strategy is to adopt a vertically diversified approach where both brands are below the market's average class. The third essay examines knowledge sharing among hotels and short-term leases managed by the same hotel management company or short-term lease host. Findings suggest that accommodations should be located near other properties managed by the same entity to facilitate operating knowledge transmission through face-to-face interactions, coordination among units, and the easy transfer of key personnel. Additionally, the study found that hotels should carefully consider entering a market with a high concentration of short-term lease ownership, as a higher concentration of short-term leases owned by the same host leads to lower hotels' RevPAR in the market.

處理類別型的資料時，常由於變異數與平均數間具有函數關係，因此資料呈現出來的變異程度會比預期的變異程度來的大，這種現象就稱為資料具有額外變異。一般的分析方法是利用廣義線性模型先作估計，再對估計之標準誤做調整。本文中將探討處理額外變異的另外兩種方法—準概似估計和隨機效果模型，並分別利用紡織原料與毒物學研究之資料作為範例來比較此兩種方法與前者的異同。 / Overdispersion is a common phenomenon in practice when modelling categorical data, and the scaled Pearson chi-square is usually used to measure it. In this study, we examine two other methods—the quasi-likelihood and the random-effect models. In addition, two examples are provided for illustration.

自1978年中國開始經濟的改革開放政策以來，吸引外資便成為其推動經濟成長的重要手段之一。1992年鄧小平南巡，再一次宣示建立「社會主義的市場經濟體制」後，由於開放的經濟政策更為明朗，使越來越多的國家到中國直接投資。自此，中國成為全世界最受歡迎的外資投資國之一。甚至在2002年超越美國，成為全世界最大的外資接受國。如此多的國家對中國展開大規模的投資，其投資的規模與投資地區的選擇卻有相當大的差異。因此，本文的研究目的，將以1993至2003年世界各國對中國投資的追蹤資料（panel data），搭配固定效果模型（fixed-effect model）與隨機效果模型（random-effect model）的估計，並以目前中國沿海發展具代表性的北京、上海與廣東三個省（市）之比較，探究影響世界各國對中國直接投資的決定因素。 實證結果發現影響外商直接投資北京、上海與廣東的決定因素中，顯著影響的變數為相對工資率、對中國貿易依存度、相對匯率以及相對借貸成本。而其餘的變數，如相對國內生產毛額、相對每人國內生產毛額和相對國家風險等皆不顯著，反映了其皆非外商直接投資中國時所考量的決定因素。 / Since 1978, China has adopted the so-called “open door policy”, attracting foreign direct investment (FDI) has become one of the most important methods to facilitate its economic growth. However, foreign countries didn’t invest large amount toward China until Deng Xiaoping’s southern trip in 1992. The purpose of this study is thus to investigate the determinants of FDI from different foreign countries to different regions in China after foreign countries started to invest tremendous amount toward China. This study adopts fixed-effect model and random-effect model to investigate the determinants of FDI in China with panel data of Beijing, Shanghai and Guangdong during the period of 1993-2003. The result shows relative wage rate, trade dependence to China, relative exchange rate and relative borrowing cost are the most important factors in attracting FDI in Beijing, Shanghai and Guangdong during 1993-2003. Finally, in order to reduce the mistakes occurred in positive models and enable the study more rigorous, then uses more methods to test the models and the result.

This thesis consists of two parts. The first part focuses on design and analysis for computer experiments and the second part deals with binary time series and its application to kinetic studies in micropipette experiments. The first part of the thesis addresses three problems. The first problem is concerned with optimal design of computer experiments. Latin hypercube designs (LHDs) have been used extensively for computer experiments. A multi-objective optimization approach is proposed to find good LHDs by combining correlation and distance performance measures. Several examples are presented to show that the obtained designs are good in terms of both criteria. The second problem is related to the analysis of computer experiments. Kriging is the most popular method for approximating complex computer models. Here a modified kriging method is proposed, which has an unknown mean model. Therefore it is called blind kriging. The unknown mean model is identified from experimental data using a Bayesian variable selection technique. Many examples are presented which show remarkable improvement in prediction using blind kriging over ordinary kriging. The third problem is related to computer experiments with nested and branching factors. Design and analysis of experiments with branching and nested factors are challenging and have not received much attention in the literature. Motivated by a computer experiment in a machining process, we develop optimal LHDs and kriging methods that can accommodate branching and nested factors. Through the application of the proposed methods, optimal machining conditions and tool edge geometry are attained, which resulted in a remarkable improvement in the machining process. The second part of the thesis deals with binary time series analysis with application to cell adhesion frequency experiments. Motivated by the analysis of repeated adhesion tests, a binary time series model incorporating random effects is developed in this chapter. A goodness-of-fit statistic is introduced to assess the adequacy of distribution assumptions on the dependent binary data with random effects. Application of the proposed methodology to real data from a T-cell experiment reveals some interesting information. These results provide some quantitative evidence to the speculation that cells can have ¡§memory¡¨ in their adhesion behavior.