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.

近年來，全球氣候變遷與環境惡化顯示目前人類活動與永續發展的衝突，如何減少能源消耗以降低溫室氣體排放成為當務之急。在都市規劃方面，以緊密都市理念探討都市發展與交通能源消耗最受到關注，然而都市發展與產業活動、家戶行為密切相關，因此若僅從交通方面分析能源消耗，恐忽略其他影響能源消耗的重要因素。故本研究著重於探討都市發展特性與能源消耗的關聯性，將產業活動與家戶行為納入模型分析，可更全面性了解都市與能源消耗的關聯性。此外，基於「全球思考、地方行動」的考量，如何落實節能減碳的目標，必須就各縣市的都市發展特性著手，透過追蹤資料模型，進一步了解各縣市的固定效果對能源消耗之影響以及能源消耗之時間趨勢。 透過對都市耗能部門進行分類，並以台灣改制後19個縣市為實證範圍，可以確認都市發展特性的幾個面向：土地使用密度、土地混合使用、交通屬性、產業屬性、家戶屬性、環境屬性與交通運輸、產業發展、家戶活動的耗能關係。實證結果發現，緊密都市有助於節能目標達成；道路增加可及性，但亦助長汽車使用，增加交通耗能；為提升大眾運輸使用率，需加強轉乘便利性以及改變私人交通運輸偏好；產業耗能與出口貿易關聯性高，不利能源減量；家戶居住行為與生活型態對能源消耗有正向影響；公園綠地能調節都市氣溫，減少耗能。 綜上所述，欲形塑一個節能減碳的都市必須透過多元途徑，在土地使用方面須維持適當的發展密度與混合程度；交通方面須加強大眾運輸便利性以提高民眾使用意願並抑制汽車使用；產業方面須透過政府獎勵節能措施及促進產業轉型以提升能源效率；家戶方面應透過教育及政策宣導以培養節能生活習慣，方能達成節能減碳的目標。 / In recent years, global climate change and environmental deterioration show a conflict between human activities and sustainable development. How to reduce energy consumption in order to mitigate greenhouse gas emission has become a top priority. According to the concept of compact city, urban planning is seen as the effective way to reduce transportation energy consumption. However, urban development is associated with industry development and household activities, so it would be improper to focus only on transportation sector. Thus the main motivation for this study is trying to illustrate urban development characteristics by combing transportation sector, industry sector and household sector, in order to understand the influence the urban development characteristics on energy consumption more comprehensively. In addition, on the concept of “global thinking, local action”, how to successfully implement energy saving policies should first understand the urban development characteristics of all counties. The purpose of this study is to empirically explore the influences of urban development characteristics on energy consumption by using panel data models, which uses the reorganization of nineteen counties areas in Taiwan as samples.In order to find out the fixed effect of all counties on energy consumption and trend of energy consumption. The empirical results show that the concepts of compact city still contributes the energy-saving goal；construction of roads increase accessibility, but also encourage car use, which increase energy consumption；to encourage the use of public transportation need to improve the convenience of transfer and change the preference of people；energy consumption in industry is highly associated to international trade, so it would be difficult for energy reduction；the trend of energy consumption has increased due to household lifestyle change；the green resources provides by park, which can adjust the temperature of city and reduce energy consumption. In sum, achieving the energy-saving city need diversified approaches, it can’t just keep increasing the density or land mixed-use. Traffic should be strengthened by improving transfer system. In order to increase the willingness to use public transport system and decrease car dependency；industry must trying to improve energy efficiency；households should cultivate the habit of saving energy by education in order to be a true energy-saving city.

近年來，隨著中國大陸經濟快速的成長，中國大陸房地產市場也隨之蓬勃發展，然而，近期中國大陸房地產價格的節節高漲，產生了房地產過熱的警訊，而區域間房地產價格的差異與不均，也成為各地區經濟發展的重要阻礙。所以，如何合理的調控房地產價格，使房地產市場能穩定成長並與經濟發展相輔相成，成為中國大陸中央政府必須持續關注與適時妥善處理的問題。 本文運用1999年至2010年中國大陸31個省市地區商品房平均銷售價格和影響房價的經濟相關變數的追蹤資料，運用雙因子固定效果模型進行實證研究。研究結果發現：中國大陸各地區財政分權程度對各地區房地產價格為非線性的關係，呈現U型的曲線。意即，存在一個財政分權的臨界值可以使房價達到最小的情況。建議中央政府應透過調整各地區的財政分權程度，來避免因財政分權不均而產生財政資源不均問題，造成房地產價格的波動。並加強對地方政府的財政預算與財政收入的監督與管制，以抑止「土地財政」的行為。 / With the economic growth, the real estate market is booming in China recently. But the overheating real estate price and the difference of real estate price between region and region become the important impediment to regional economic development in China. Therefore, the central government how to control the real estate price is an immense problem. By using the panel data of average selling price of commercialize buildings and the economic variables of 31cities in China between 1999 and 2010, the study uses two-way fixed effects model to investigate the effect of fiscal decentralization on the real estate price in China. The empirical analysis’ result shows that the fiscal decentralization provides a non-linear effect on the real estate price, it presents the U-shaped curve. In other words, there are a degree of fiscal decentralization can make the real estate price to reach the minimum. According to results, we propose to take some policies. The central government should adjust the degree of fiscal decentralization in each region, in order to avoid the local government financial problem to lead to rise the real estate price.

中國大陸自改革開放之後，其經濟成長之快速，成功歸究於自由的市場經濟。然而2008金融海嘯爆發後，使得各國開始檢視過度的分權和市場自由化的適度性。中央政府和地方政府，如何在權利和稅收的分配上，達成一最適的規模？如何有效運用財政地方分權？將是一門複雜且重要的議題。 本研究之研究目的主要有以下幾點：第一，希望能藉由相關理論文獻，解釋財政地方分權對中國省、市之間地方財政赤字是否會有所影響或是關聯，將以此為本研究之理論基礎，並且進行實證的檢驗；第二，將以1994年以後中國財政改革以後之財政相關資料，利用中國31個省、市的追蹤資料，資料蒐集期間涵蓋1995年至2010年，以各年各省、市的財政赤字作為衡量該省、市的財政情形，以期能夠了解各地區財政的影響情形； 第三，本研究建立一個二因子固定效果模型，來檢視中國31個省市財政分權對其地方財政赤字的影響，並將各省市的情形做歸納；第四，從實證模型中發現，財政地方分權與地方財政赤字間的關聯性為一非線性關係且具有U型曲線關係；最後，利用實證結果來提供具體的政策建議。 / Fiscal decentralization is considered as one of the successful institutional reforms to promote the development of China.In order to attract the resource, the regions have to improve or maintain their finance. Therefore, what issues will improve the provincial local government deficit becoming an immense problem. How to use fiscal decentralization tool？It will be an important issue. The research purpose of this literature is to use the empirical model with the panel data which includes 31 provinces, cities and regions in China during the period of 1995 to 2010 as well as to search the following question. First , analyse the provincial local government deficit and find out the determinative factors of regional deficit. Second, in order to realize the precise relationship between the fiscal decentralization and the provincial local government deficit, this study establishes 2 ways fixed effect model. Finally, to reducing the mistakes occurred in positive models and enabling the study more rigorous, this study uses more methods to test the models and the result. Finally, to reducing the mistakes occurred in positive models and enabling the study more rigorous, this study uses more methods to test the models and the result.

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.

本研究探討選擇權交易量之大小對於波動度預測之效率性所造成之對偶效果(dual effect)，驗證〝正常的高交易量〞與〝異常的高交易量〞對於波動度預測能力是否有不同的影響。本研究採用panel data之資料型態，以LIFFE上市的個股買權為對象，資料長度為三年左右。主要欲探討之假說為： 1.一般而言，交易量大的選擇權，其波動度估計誤差較交易量小的選擇權來得小。 2.相對於平日水準而言，某日交易量異常高的選擇權將有較大的波動度估計誤差。 本研究所使用的波動度預測模型為隱含波動度(ISD)，採用的是最接近到期月份及最接近價平的合約。實證以組合迴歸、固定效果模型、隨機效果模型分別估計之，加以比較。結果發現固定效果模型為較佳之解釋模型，然而結果顯示交易量的對偶效果並不明確影響波動度預測誤差，故推測有某種影響公司間差異的因素，即公司間之異質性，比相對交易量更容易影響波動度預測之誤差。另外，透過組間與組內效果之分析，發現不論是長期還是短期，由於公司間的異質性存在，使得相對交易量對於波動度預測誤差均無明顯影響。 / The purpose of this research is to study the dual effect on the efficiency of volatility forecasting which is caused by the volume of option market, with the intent to test whether〝normal high volume〞and〝abcdrmal high volume〞cause different results on the ability of volatility forecasting. The data used is in the form of panel data. It is drawn from LIFFE, and has a length of about three years. The hypotheses to be examined in this study are：1. High-average-volume options have smaller volatility forecasting errors than low-average-volume options; 2. Options have larger volatility forecasting errors on abcdrmally-high-volume days than on normal-volume days. In this research, volatility is forecasted by implied standard deviation (ISD) which is implied in the at-the-money and the nearest expiry month options. Pooled regression、fixed effect model、and random effect model methods were applied. The results show that the fixed effect model made the best analysis amongst the three models. However, the result does not support the hypotheses made above, which means that volume does not have much influence on volatility forecasting error. It is inferred that there exists some other factors which could cause the difference between firms, namely heterogeneity, and these factors have much more powerful influence over volatility forecasting error than volume. Finally, it was found that no matter for long run or short run, because of the existence of heterogeneity, relative volume doesn’t have obvious influence on volatility forecasting errors when analyzing the difference between the between-individual effect and the within-individual effect.