This study focuses on how spatial circumstances affect property owners’ preference toward sustainable urban development, in the form of three-essays. In the first essay, property owners’ preference toward the concept of compact development is identified. Compact development is an increasingly popular concept that includes multiple aspects, such as mixed land use, high density, and pedestrian/transit-friendly options. Previous hedonic literature on the comprehensive effect of compact development is limited. Also, spatial dependence in the data, something likely endemic to compact development, has not yet been thoroughly addressed. This study uses a spatial fixed-effect model, a spatial-autoregressive model with auto-regressive disturbances (SARAR), and a spatial fixed-effect SARAR model to determine the price effect of “compactness” in a major U.S. metropolitan area. By analyzing of 47,000 sales records in Fulton County over a decade, this study indicates that home buyers prefer to have smaller, more diffuse greenspace nearby, rather than a large, concentrated greenspace at a longer walking distance. High parcel density and diverse land use is consistently disvalued, and the premium on accessing public transportation is not identified among all models. No specific trend over time has been observed, despite the recession starting in 2008. Finally, a comprehensive index of compactness shows relatively high willingness-to-pay for compact development. The second essay tests the spatial spillover of signaling within the pursuit of LEED certification. The benefit of pursuing green building certification mainly comes from two aspects: the cost-effectiveness from energy efficiency and the signaling consideration, including the premium on property values, benefits from a better reputation, morality values, or purely pride. By analyzing all new constructions that received LEED certification from 2000 to 2012 (LEED-NC v2.0 to v2.2) in the U.S., this study tries to identify the size of the signaling effects, and spillover of signaling, as building owners pursue LEED certification. The results show that the signaling effect affects decision making in pursuing LEED certification, especially at scores around thresholds. The size of signaling effects differs among different owner types and different certificate levels. For the Gold level or below, government and non-profit-organization owners value signaling more than do profit-seeking firms. At the Platinum level, there is no significant difference among owner types. This study also finds that the signaling effect clusters spatially for government and profit-seeking firms. Finally, the results show that the cluster of signaling is independent from the cluster of LEED buildings, indicating that mechanisms behind the cluster of signaling are different from those of LEED constructions. The third essay tests the distance effect on the support for Atlanta BeltLine. Atlanta BeltLine, a large urban redevelopment project currently underway in the center of Atlanta, transforms 22 miles of historical railroad corridors into parks, trails, pedestrian-friendly transit areas, and affordable housing. This study aims to determine the distance effect on the support of Atlanta BeltLine and whether the implement of Tax Increment Financing (TIF) affects the support. The contributions of this exercise are twofold. First, it demonstrates the risks and remedies to missing spatial data by solving the technical problem of missing precise spatial location values. Second, it tests underlying reasons why distance can help explain the level of support that Atlanta BeltLine has received, with striking implications for theories like the Homevoter hypothesis. Survey data used in this study was conducted in summer 2009, about three years after the declaration of the project. The support by both homeowners and renters significantly declines as distance from the BeltLine increases. However, when residents’ tendency to use BeltLine parks and transits is entered as a variable, the distance effect disappears. By indicating that the distance effect comes from homeowners’ and renters’ the accessibility to BeltLine amenities, the result rejects the homevoter hypothesis, which holds that property value increment is the main mechanism behind support. The results also show that whether or not a homeowner or renter is a parent in City of Atlanta affects a person’s support of the BeltLine. These results lead to the conclusion that the concern of TIF affecting future school quality hampers the support of the project.

銅價在近年來不斷的創下歷史新高，由於台灣蓬勃的電子、半導體、工具機產業皆需要銅，因此銅進口量位居全球第五(ICSG,2009)，使得台灣企業的生產成本受國際銅價的波動影響甚鉅，全球有70%的銅價是按照英國倫敦金屬交易所(London Metal Exchange, LME)的牌價進行貿易，因此本研究欲建置預測模式以預測銅價未來趨勢。 　　本研究之資料來源為2003年1月2日至2011年7月14日的LME三月期銅價，並依文獻探討選取LME的銅庫存、三月期鋁價、三月期鉛價、三月期鎳價、三月期鋅價、三月期錫價，以及金價、銀價、石油價格、美國生產者物價指數、美國消費者物價指數、聯邦資金利率作為影響因素的分析資料。時間序列分析、類神經網路已被廣泛的用於預測股市及期貨，本研究先藉由向量自我迴歸模型篩選出有影響力的變數，同時建置GARCH時間序列預測模型與具有遞迴的Elman類神經網路預測模型，再整合兩者建置GARCH-Elman類神經網路預測模型。 本研究之向量自我迴歸模型顯示銅價與金、鋁、銅庫存前第1期；自身前第2期；鎳、錫前第3期；鋅前第4期的變動有負向的影響；受到石油前第2期的變動有正向的影響，這其中以銅的自我解釋變異最高，銅庫存最低，推測其影響已有效率地反映到銅價上。也驗證預測模型必須考量總體經濟變數，且變數先經向量自我迴歸模型的篩選能因減少雜訊而提升類神經網路的預測能力。依此建置的GARCH模型有33.81%的累積報酬率、Elman類神經網路38.11%、整合兩者的GARCH-Elman類神經網路56.46%，皆優於實際銅價指數的累積報酬率。對銅有需求的企業者，能更為準確的預測漲跌趨勢，依此判斷如何跟原物料供應商簽訂合約的價格與期間，使其免於價格趨勢的誤判而提高生產成本，並提出五點建議供未來研究者參考。 / The recent copper price in London Metal Exchange (LME) has breaking the historical high. Taiwan’s booming electronics, semiconductor and machine tool industry causing copper import volume ranked fifth in the world (ICSG, 2009). Because of 70% of copper worldwide trade in accordance with the price of the London Metal Exchange, this study using time series and neural networks to build the LME copper price forecast model. 　　This study considering copper, copper stocks, aluminum, lead, nickel, zinc, tin, gold, silver, oil ,federal funds rate, CPI and PPI during the period of 2003/1/2 to 2011/7/14. Time series model and neural networks have been widely used for forecasting the stock market and futures. In this study, using Vector Autoregressive (VAR) model screened influential variables, building GARCH model and Elman neural network to forecast the LME copper price; and further, integrating this two models to build GARCH-Elman neural network prediction model. 　　This study’s VAR models show that the copper has negative effect with gold, aluminum, copper stocks, nickel, tin, zinc and itself. And has positive impact with oil prices. The highest of explained variance is copper. Copper stocks are lowest, speculating that its impact has been efficiently reflecting on the price of copper. Verifying the prediction model must consider the macroeconomics variables. Using VAR model screened influential variables can reduce noise to enhance the predictive ability of the neural network. This study’s GARCH model has 33.81% of the cumulative rate of return, Elman neural network has 38.11% and the GARCH-Elman neural network has 56.46%. All of them are better than the actual price of copper.

Government fiscal and monetary policy has long been based on the theory that was neither proven nor refuted since its origination. The original form of the Phillips curve has undergone significant modifications but its relevance remains questionable. This thesis examines the correlation between inflation and unemployment observed in the Czech Republic and Germany over the last twenty years. The validity of the theory is tested by advanced methods of time series analysis in the R environment. All the variables are gradually tested which results in the assessment of the correlation between the time series. The outcome of the testing is presented for both countries and a comparison at international level is drawn. Is is discovered that both of the countries have dependencies in their data. Czech republic has significant dependency in both ways, for Germany is the dependency significantly weaker and only in one way.

Cette thèse étudie les performances du marché du travail dans une économie susceptible de présenter plusieurs équilibres, et les implications d’une telle configuration pour la politique économique. Elle comporte quatre essais, traitant chacun d’un aspect spécifique de cette problématique. En premier lieu, l’analyse économétrique des séries temporelles de taux de chômage de quelques pays de l’OCDE, permettant notamment l’identification des changements de régimes et de leurs caractéristiques, apporte des évidences significatives à l’appui de l’hypothèse d’une multiplicité d’équilibres. En second lieu, on étudie les effets de l’introduction d’un salaire minimum obligatoire et d’une hausse de celui-ci dans un modèle statique de concurrence imparfaite avec négociations salariales au niveau de la firme, le facteur travail étant hétérogène. Si la hausse du salaire minimum est défavorable à l’emploi,l’introduction d’un salaire minimum en présence d’une multiplicité d’équilibres permet d’éliminer l’équilibre Pareto-inférieur. En troisième lieu, on étudie également les implications de l’existence d’équilibres multiples pour les politiques économiques, du fait de l’altération des propriétés dynamiques de l’économie, à travers l’analyse complète d’un modèle dynamique de concurrence imparfaite avec des négociations salariales individuelles et des frictions d’appariement sur le marché du travail. Enfin, on montre grâce à l’outil expérimental dans quelle mesure l’introduction d’une variable dite de tâche solaire, peut être source de défaut de coordination et d’inefficience dans une économie possédant deux équilibres Pareto-ordonnés. / This thesis analyzes the performances of labor market in an economy subject to multiple equilibria and the implications of such a configuration for economic policy. It contains four pieces of research, each dealing with a particular aspect of the general setting. First, the econometric analysis of the unemployment time series for several OECD countries,which allows the identification of regime switches and their characteristics, brings forth some significant evidence that the multiple equilibria framework is relevant. Second, the effect of the implementation and of the rise of the minimum wage are investigated through a static model, assuming imperfect competition, heterogeneous labor input and wage negotiations at the firm level. Though minimum wage hikes have an adverse effect on employment, the implementation of a binding minimum wage turns out to be an efficient tool for excluding the Pareto- inferior equilibrium. Third economic policy conditions are also affected because the existence of multiple equilibria alters the dynamic properties of the economy. This case has been investigated in the framework of a fully dynamic model assuming imperfect competition individual wage negotiations and matching frictions. Finally, a coordination game experiment confirms that the introduction of a sunspot can be a source of coordination failure and inefficiency in an economy with two Pareto-ranked equilibria.

El presente trabajo de investigación tiene como objetivo evaluar la fuente de energía (renovable y no renovable) que fomente en mayor medida el crecimiento económico en el Perú. Para ello, se observó la relación entre el crecimiento económico y las diferentes fuentes de energía en el corto plazo y el largo plazo. Además, se utilizó el método autorregresivo con retardos distribuidos (ARDL) para confirmar la relación a largo plazo de las series. El modelo ARDL confirmó la cointegración entre las variables y con ello, la relación a largo y corto plazo. Los hallazgos que arrojó la estimación afirman que el consumo de energía renovable se relaciona positivamente con el PBI en el corto y largo plazo. Por lo tanto, se concluye que la economía peruana debería invertir aún más en la exploración y explotación de recursos de energía renovable. / The objective of this research work is to evaluate the source of energy (renewable and non-renewable) that promotes economic growth in Peru to a greater extent. For this, the relationship between economic growth and different energy sources in the short and long term was observed. In addition, the autoregressive distributed lag method (ARDL) was used to confirm the long-term relationship of the series. The ARDL model confirmed the cointegration between the variables and with it, the long- and short-term relationship. The findings that the estimation yielded affirm that the consumption of renewable energy is positively related to the GDP in the short and long term. Therefore, it is concluded that the Peruvian economy should invest even more in the exploration and exploitation of renewable energy resources. / Trabajo de investigación

No description available.

As more devices with different service requirements become connected to networked systems, such as Internet of Things (IoT) devices, maintaining quality of service becomes increasingly difficult. Large data sets can be obtained ahead of time in networks to train prediction models offline, however, resulting in high computational costs. Online learning is an alternative approach where a smaller cache of fixed size is maintained for training using sample selection algorithms, allowing for lower computational costs and real-time model re-computation. This project has resulted in two newly designed sample selection algorithms, Binned Relevance and Redundancy Sample Selection (BRR-SS) and Autoregressive First, In First Out-buffer (AR-FIFO). The algorithms are evaluated on data traces retrieved from a Key Value store and a Video on Demand service. Prediction accuracy of the resulting model while using the sample selection algorithms and the time to process a received sample is evaluated and compared to the pre-existing Reservoir Sampling (RS) and Relevance and Redundancy Sample Selection (RR-SS) with and without model re-computation. The results show that, while RS maintains the lowest computational overhead, BRR-SS outperforms both RS and RR-SS in prediction accuracy on the investigated traces. AR-FIFO, with its low computational cost, outperforms offline learning for larger cache sizes on the Key Value data set but shows inconsistencies on the Video on Demand trace. Model re-computation results in reduced error rates and significantly lowered variance on the investigated data traces, where periodic model re-computation overall outperforms change detection in practicality, prediction accuracy, and computational overhead. / Allteftersom fler enheter med olika servicekrav ansluts till nätverkssystem, såsom Internet of Things (IoT) enheter, ökar svårigheten att erhålla nödvändig servicekvalitet. Nätverk kan ge upphov till stora datamängder för träning av prediktionsmodeller offline, dock till en hög beräkningskostnad. Ett alternativt tillvägagångssätt är onlineinlärning där en mindre cache av fast storlek upprätthålls för träning med hjälp av datapunkturvalsalgoritmer. Detta möjliggör lägre beräkningskostnader samt realtidsmodellomräkningar. Detta projekt har resulterat i två nydesignade datapunkturvalsalgoritmer, Binned Relevance and Redundancy Sample Selection (BRR-SS) och Autoregressive First In, First Out-buffer (AR-FIFO). Algoritmerna utvärderas på dataspår som hämtats från ett Key Value-lager och en Video on Demand-tjänst. Förutsägelseförmåga för den resulterande modellen när datapunkturvalsalgoritmerna används och tid för bearbetning av mottagen datapunkt utvärderas och jämförs med dem redan existerande Reservoir Sampling (RS) och Relevance and Redundancy Sample Selection (RR-SS), med och utan modellomräkning. RS resulterar i lägst beräkningskostnad medan BRR-SS överträffar både RS och RR-SS i förutsägelseförmåga på dem undersökta spåren. AR-FIFO, med sin låga beräkningskostnad, överträffar offlineinlärning för större cachestorlekar på Key Value-spåret, men visar inkonsekvent beteende på Video on Demand-spåret. Modellomräkning resulterar i mindre fel och avsevärt sänkt varians på dem undersökta spåren, där periodisk modellomräkning totalt sett överträffar förändringsdetektering i praktikalitet, förutsägelseförmåga och beräkningskostnad. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

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.

Our intention is to find similarity among the time series expressions of the genes in microarray experiments. It is hypothesized that at a given time point the concentration of one gene’s mRNA is directly affected by the concentration of other gene’s mRNA, and may have biological significance. We define dissimilarity between two time-series data set as the variance of Euclidean distances of each time points. The large numbers of gene expressions make the calculation of variance of distance in each point computationally expensive and therefore computationally challenging in terms of execution time. For this reason we use autoregressive model which estimates nineteen points gene expression to a three point vector. It allows us to find variance of difference between two data sets without point-to-point matching. Previous analysis from the microarray experiments data found that 62 genes are regulated following EGF (Epidermal Growth Factor) and HRG (Heregulin) treatment of the MCF-7 breast cancer cells. We have chosen these suspected cancer-related genes as our reference and investigated which additional set of genes has similar time point expression profiles. Keeping variance of difference as a measure of distance, we have used several methods for clustering the gene expression data, such as our own maximum clique finding heuristics and hierarchical clustering. The results obtained were validated through a text mining study. New predictions from our study could be a basis for further investigations in the genesis of breast cancer. Overall in 84 new genes are found in which 57 genes are related to cancer among them 35 genes are associated with breast cancer.