Before a government makes economic policies, it must first fully understand the causality between energy consumption and economic growth. This study uses Chow Test, Unit Root Test, Co-integration Test, Vector Autoregressive Model, Vector Error Correction Model, Granger Causality Test, Impulse Response Function and Variance Decomposition to examine whether the relationships between energy consumption and economic growth for Taiwan and Korea had changed after the Asian Financial Crisis of 1997, in order to understand whether their economic policies have changed in response. Taiwan¡¦s energy consumption and GDP had one-way effect ¡V that is, her energy consumption affected GDP but not vice versa ¡V while that of South Korea exhibited a two-way relationship. However, after the Crisis, such relationship for Taiwan had changed to that of two-way. The relationship between energy consumption and GDP for South Korea remained two-way after the Crisis.

Using the monthly data between 1999 and 2007 in Taiwan, we examine the relationship of exchange rate, trade and capital flow in this paper. Granger causality test and impulse response from vector autoregressive model are employed to obtain the short-run dynamics among the variables, and Johansen cointegration test and error correction model are applied to study the long-run equilibrium. This paper reconfirms the J-curve effect in the short run and the validity of Marshall-Lerner condition in the long run. Our results also show the negative correlation of capital flow and the nominal effective exchange rate. Limited by the slow adjustment speed of trade balance, exchange rate and capital flow are the major drives back to equilibrium when the system deviates from the long-run equilibrium. Further, the capital flow variables are the leading indicators of the others in the most cases. However, different capital flow variables induce different patterns of dynamics in the short-run.

Trade deficit, financial deficit, and current account deficit of the United States have all been problems deeply concerned by economists and politicians in recent decades. Since the third season of 2000, a recession of the United States and the whole world has gradually started to appear. In addition, as a result of the 9/11 terrorist attacks and the war in Iraq the stock market has begun to decline significantly. In order to promote the recovery of its economy, the federal government determines to adopt the expanded financial policy which will most likely in the end cause its financial deficit more serious. The main purpose of this paper is to investigate the factors that influence the current account deficit of the United States. Because the study considers foreign variables that related researches ignore, we choose five variables as follows: regional output differential, regional interest rate differential, terms of trade, regional real effective exchange rate, and current account. Therefore, we adopt the Unit Root Test, the Granger Causality Test, the Co-integrating Test, and SVAR (Structural Vector Autoregressive) model to run RATS and E-views. It is the finding of empirical result that the United States government considers terms of trade and current account that can't be quantized of the first importance rather than the exchange rate factor that general research is thought. This is one of the contributions of the study.

¡@¡@The purpose of this research is to employ the STAR model in discussing and analyzing the relationship between stock index and macroeconomic variables in Taiwan, Japan and Korea. ¡@¡@Monthly stock market index data is analyzed over the period January 1990 to December 2000, with the sample period from January 2001 to April 2005 being used in an out-of -sample forecasting exercise. The macroeconomic variables considered in this paper include money supply, consumer price index, industrial production index, interest rate and exchange rate. ¡@¡@The empirical results of Taiwan, Japan and Korea show that LSTAR & ESTAR model improve both the in-sample fit and out-of-sample forecast of the data over both the linear model alternative.

This paper compares the performance of Bayesian variable selection approaches for spatial autoregressive models. We present two alternative approaches which can be implemented using Gibbs sampling methods in a straightforward way and allow us to deal with the problem of model uncertainty in spatial autoregressive models in a flexible and computationally efficient way. In a simulation study we show that the variable selection approaches tend to outperform existing Bayesian model averaging techniques both in terms of in-sample predictive performance and computational efficiency. (authors' abstract) / Series: Department of Economics Working Paper Series

In the first part of this study, two entropy methods under different distribution assumptions are examined on a network of stream gauging stations located in Kizilirmak Basin to rank the stations according to their level of importance. The stations are ranked by using two different entropy methods under different distributions. Thus, showing the effect of the distribution type on both entropy methods is aimed. In the second part of this study, autoregressive models with asymmetric innovations and an artificial neural network model are introduced. Autoregressive models (AR) which have been developed in hydrology are based on several assumptions. The normality assumption for the innovations of AR models is investigated in this study. The main reason of making this assumption in the autoregressive models established is the difficulties faced in finding the model parameters under the distributions other than the normal distributions. From this point of view, introduction of the modified maximum likelihood procedure developed by Tiku et. al. (1996) in estimation of the autoregressive model parameters having non-normally distributed residual series, in the area of hydrology has been aimed. It is also important to consider how the autoregressive model parameters having skewed distributions could be estimated. Besides these autoregressive models, the artificial neural network (ANN) model was also constructed for annual and monthly hydrologic time series due to its advantages such as no statistical distribution and no linearity assumptions. The models considered are applied to annual and monthly streamflow data obtained from five streamflow gauging stations in Kizilirmak Basin. It is shown that AR(1) model with Weibull innovations provides best solutions for annual series and AR(1) model with generalized logistic innovations provides best solution for monthly as compared with the results of artificial neural network models.

L'imagerie médicale échographique présente plusieurs avantages comme son innocuité, sa facilité d'emploi, la diversité des organes concernés et le faible coût de ce mode d'imagerie. Cependant les images obtenues par échographique souffrent d'une résolution plutôt faible comparées à celle que l'on peut obtenir avec un appareil d'IRM ou en utilisant des rayons X. Le défi majeur de l'échographie médicale est donc de réussir à produire des images avec une résolution beaucoup plus fine, à fréquence nominale fixe.Des travaux ont été entrepris dans ce sens depuis longtemps. Plusieurs pistes ont été explorées. La majorité des travaux effectués jusqu'à présent a consisté à travailler sur l'échographe et particulièrement sur les sondes ultrasonores, avec principalement pour objectif d'augmenter la fréquence des ultrasons utilisés. Cette approche a conduit à l'existence de l'échographie haute résolution, avec cependant une limite importante qui est celle de la profondeur d'exploration.Une autre approche consiste à traiter numériquement des images échographiques classiques pour améliorer leur résolution. Cette méthode a plusieurs avantages, elle permet notamment de contourner la difficulté causée par la réduction de profondeur de champ liée à l'augmentation de la fréquence ultrasonore.Dans cette thèse, nous présentons une méthode permettant d'améliorer la résolution des images échographiques. Le travail de thèse à consister à adapter cette méthode à l'imagerie échographique et à proposer une estimation de la résolution théorique maximale atteinte par cette méthode en fonction de paramètres de l'image dont le SNR, et la largeur de bande de la PSF. Nous avons également proposé une méthode de superrésolution adaptée aux ultrasons. Par son apport sur l'amélioration théorique de la superrésolution et par l'adaptation au cas particulier de l'imagerie ultrasonore, ce travail de thèse ouvre des perspectives sur l'amélioration de la résolution des images échographiques par traitement du signal et de l'image. / Medical Imaging Ultrasound has several advantages such as its safety, ease of use, the diversity of organs that can be imaged and the low cost of this imaging mode. However, the images obtained by ultrasound suffer from relatively low resolution compared to others than can be obtain with an MRI or using X-rays. The major challenge of medical ultrasound is the ability to produce images with a resolution much finer, without modifying the nominal frequency.Work has been undertaken in this direction for some time. Several approaches have been explored. Most of the work done so far has been to work on the ultrasound acquiring device and particularly on ultrasonic probes, with main objective to increase the frequency of ultrasound used. This approach has led to the existence of high-resolution ultrasound, but with the reduction of the depth of exploration as an important limitation.Another approach is to treat numerically conventional ultrasound images to improve resolution. This method has several advantages, it allows to circumvent such difficulties caused by the reduction of depth of field due to the increase in the ultrasonic frequency.In this thesis, we present a method to improve the resolution of ultrasound images. The thesis to be to adapt this method to ultrasound imaging and to provide an estimate of the maximum theoretical resolution achieved by this method based on image parameters including SNR and the bandwidth of the PSF. We also proposed a method of superresolution suitable for ultrasound. By providing on improving theoretical superresolution and adaptation to the particular case of ultrasound, this thesis opens up on improving the resolution of ultrasound images by processing the signal and the image.

In this paper, we explore the relationship between state-level household income inequality and macroeconomic uncertainty in the United States. Using a novel large-scale macroeconometric model, we shed light on regional disparities of inequality responses to a national uncertainty shock. The results suggest that income inequality decreases in most states, with a pronounced degree of heterogeneity in terms of shapes and magnitudes of the dynamic responses. By contrast, some few states, mostly located in the West and South census region, display increasing levels of income inequality over time. We find that this directional pattern in responses is mainly driven by the income composition and labor market fundamentals. In addition, forecast error variance decompositions allow for a quantitative assessment of the importance of uncertainty shocks in explaining income inequality. The findings highlight that volatility shocks account for a considerable fraction of forecast error variance for most states considered. Finally, a regression-based analysis sheds light on the driving forces behind differences in state-specific inequality responses. / Series: Working Papers in Regional Science

Submitted by Israel Vieira Neto (israel.vieiraneto@ufpe.br) on 2015-03-06T14:22:22Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DISSERTAÇÃO GIUSEPPE TREVISAN.pdf: 3587579 bytes, checksum: fa47c846ce99688bf17f94c5df29eb87 (MD5) / Made available in DSpace on 2015-03-06T14:22:22Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DISSERTAÇÃO GIUSEPPE TREVISAN.pdf: 3587579 bytes, checksum: fa47c846ce99688bf17f94c5df29eb87 (MD5) Previous issue date: 2013-03-08 / FACEPE / Um segmento importante da literatura de Economia do Crime afirma que, além das variáveis socioeconômicas, o espaço é fator fundamental a se associar com a criminalidade. Dada a falta de evidências empíricas sobre a relação entre espaço e crime, este estudo tem por objetivo identificar as correlações entre as variáveis ambientais com a taxa de homicídio nos bairros da cidade do Recife. Para isso, foram construídas variáveis de ambiente que capturam características relacionadas à distribuição dos tipos de domicílios dos bairros do Recife e foi implementada a técnica de econometria espacial para averiguar efeitos de spillover espaciais. O modelo SAR apresenta o melhor ajuste e mostra que a proporção de estabelecimentos nãoresidenciais em relação ao total de estabelecimentos do bairro tem uma relação positiva com a criminalidade e a concentração de domicílios residenciais está associada a índices de criminalidades mais baixos. As correlações das variáveis socioeconômicas seguem o padrão da maioria dos achados da literatura nacional e internacional, exceto para o caso da densidade demográfica.

Nous proposons d’étudier la méthodologie de la segmentation de processus avec un bruit autorégressif sous ses aspects théoriques et pratiques. Par « segmentation » on entend ici l’inférence de points de rupture multiples correspondant à des changements abrupts dans la moyenne de la série temporelle. Le point de vue adopté est de considérer les paramètres de l’autorégression comme des paramètres de nuisance, à prendre en compte dans l’inférence dans la mesure où cela améliore la segmentation.D’un point de vue théorique, le but est de conserver un certain nombre de propriétés asymptotiques de l’estimation des points de rupture et des paramètres propres à chaque segment. D’un point de vue pratique, on se doit de prendre en compte les limitations algorithmiques liées à la détermination de la segmentation optimale. La méthode proposée, doublement contrainte, est basée sur l’utilisation de techniques d’estimation robuste permettant l’estimation préalable des paramètres de l’autorégression, puis la décorrélation du processus, permettant ainsi de s’approcher du problème de la segmentation dans le cas d’observations indépendantes. Cette méthode permet l’utilisation d’algorithmes efficaces. Elle est assise sur des résultats asymptotiques que nous avons démontrés. Elle permet de proposer des critères de sélection du nombre de ruptures adaptés et fondés. Une étude de simulations vient l’illustrer. / We propose to study the methodology of autoregressive processes segmentation under both its theoretical and practical aspects. “Segmentation” means here inferring multiple change-points corresponding to mean shifts. We consider autoregression parameters as nuisance parameters, whose estimation is considered only for improving the segmentation.From a theoretical point of view, we aim to keep some asymptotic properties of change-points and other parameters estimators. From a practical point of view, we have to take into account the algorithmic constraints to get the optimal segmentation. To meet these requirements, we propose a method based on robust estimation techniques, which allows a preliminary estimation of the autoregression parameters and then the decorrelation of the process. The aim is to get our problem closer to the segmentation in the case of independent observations. This method allows us to use efficient algorithms. It is based on asymptotic results that we proved. It allows us to propose adapted and well-founded number of changes selection criteria. A simulation study illustrates the method.