Doctor of Philosophy / Department of Economics / Wayne Nafziger / This dissertation constructs three empirical essays. The first essay illustrates the causality on the relationship between output (GDP) growth and exports. By using the Modified Wald (MWald) test we observe unidirectional causality from exports to GDP. More specifically, for the robustness we use a Vector Error Correction Model (VECM) model and the Generalized Impulse Response Function Analysis (GIRA). The VECM and the GIRA yield bidirectional causality between exports and GDP, which weakly supports the unidirectional result of the to MWald test. Meanwhile, we confirm that there is structure break by using the structural break test. These results are plausible and consistent with the expectations of our study for the Export Led Growth Hypothesis (ELGH). However, compared with previous studies on the ELGH for Korea, our results are different. Other studies show a bidirectional causality relationship but this study only has unidirectional causality. These differences may be caused from different observation data, various variables, and use of different econometric methodologies. Also, model selection and omitting variables can also significantly change the results of causality testing. The second essay investigates a degree of competition between Korea's and China's exports in the U.S. market by using the substitute elasticity on a simple demand model. The market share of Korean exports has been decreasing while that of China's has been increasing. The results of this study are as follows. First, we find that Korea has a dominant market share of only goods group code 27 in commodity groups over that of China, otherwise having China's dominant market shares over those of Korea for other export sections by using historical trade data. Second, most estimates of substitute elasticity between both countries' exports in the U.S. market are small (inelastic). However, 61 (apparel articles and accessories, knit or crochet), 62 (apparel articles and accessories, not knit etc) and 85 (electric machinery etc, sound equipments, TV equipment, parts) commodity groups' substitute elasticities are large (elastic) and are competitive in the U.S. market compared with those of China. A small value of the elasticity of substitution may be due to an identification problem for a simple standard model as well as measurement errors in prices as a unit value in this study. So, in order to avoid problems such as these, we may need to use appropriate instrumental or proxy variables in the simple standard model, which highly correlate with the independent (unit price) variables and are uncorrelated with measurement error terms. In practice, it is not easy to find good instrumental variables. The final essay evaluates the roles of price and income as important factors that affect Korea's exports by using the most recent monthly data. By using the Autoregressive Distributed Lag (ARDL) bounds testing approach we find the long-run relationship of variables and estimate the long-run price and income elasticities. However, the estimates of these long-run elasticities are statistically insignificant. This may be due to some misspecifications or measurement errors in our model. Meanwhile, due to the existence of the long-run relationship between variables, we construct the Error Correction Model (ECM) in order to observe the short-run dynamics of the elasticities. Specifically, we add a dummy variable into our export demand model to achieve more efficient estimations since the dummy variable reflects a shock in Korea's export; Korea's economic crisis in 1997. In contrast to the long-run elasticity, we find that the short-run elasticities' estimates are more statistically significant. When we use the structure break test to check the structural stability of Korea's export demand, we find that there is no structural break point of 1997. Therefore, a shock of Korea's economic crisis in 1997 might not significantly affect Korea's export demand in a given sample. However, the Information Technology (IT) bubble of the world economy in 2001 and the entry of Korea into the OECD had triggered an increase in Korea's export demand due to existing structural break points of both events. In addition, we find that income elasticities are larger than price elasticities in the short run. This implies that income has more of an impact than that of price for the export demand model in the short run. This also implies that the change of Korea's exports in the short run is more sensitive to changes in foreign income (industrial production) compared with that of price (exchange rate). An interesting result, thus, is that Korea's exports in the short run may have higher export performance on income than that of price (exchange rate). This might be a consequence of the dependence of an increase in foreign income in recent years. In recent years, developing countries have greatly increased their economic growth compared with that of developed countries and Korea's exports have increased into these developing countries. Thus, we confirm that an increase in Korea's exports is mainly affected by income compared with price, specifically in the short run by using recent data.

Perfusion magnetic resonance imaging is modern diagnostic method used mainly in oncology. In this method, contrast agent is injected to the subject and then is continuously monitored the progress of its concentration in the affected area in time. Correct determination of the arterial input function (AIF) is very important for perfusion analysis. One possibility is to model AIF by multichannel blind deconvolution but the estimated AIF is necessary to be scaled. This master´s thesis is focused on description of scaling methods and their influence on perfussion parameters in dependence on used model of AIF in different tissues.

No description available.

There are several reasons for using non-Cartesian k-space sampling methods in Magnetic Resonance Imaging (MRI). Such a method is radial sampling, which includes the advantage of continuous coverage of the k-space center which results in higher robustness to motion. On the other hand, radial imaging does have some limitations that must be considered. The method is more sensitive to gradient imperfections, such as eddy currents and gradient delays, resulting in inconsistencies between the nominal and actual gradient waveforms. This leads to distortions in the sampling trajectory, also called trajectory errors, yielding reconstructed images with artifacts caused by the gradient imperfections. The aim of this project was therefore to implement a method that takes these errors into account and perform a correction of the trajectory errors to yield images with reduced artifacts. Various methods have been proposed for correction of the gradient errors, some more effective than others. The method implemented in this project was based on the gradient impulse response function (GIRF) which characterizes the gradient system responses. When GIRF was acquired, the actual gradient waveforms played-out during the imaging measurement could be predicted by first modeling the nominal gradient waveforms and then performing a convolution with the corresponding GIRF for each gradient axis. The imaging experiments involved measurements on two different resolution phantoms and in-vivo measurements to note possible differences in correction performance. The used pulse sequences for imaging were FLASH and bSSFP. The results showed that the applied method using GIRF did reduce the artifacts caused by gradient imperfections in the reconstructed images taken with the FLASH sequence. On the other hand, the results for the bSSFP sequence were not as successful due to incomplete modeling of the gradient waveforms. The conclusion to be drawn is that the GIRF-correction does adequately compensate for the trajectory errors when using a radial sampling trajectory for the FLASH sequence and hence yield images with almost eliminated artifacts. A suggestion for future work would be to further investigate the bSSFP sequence modeling to obtain better bSSFP-images. / Det finns flera anledningar till att använda icke-Kartesiska k-space samplingsmetoder i magnetisk resonanstomografi. En sådan metod är radiell sampling, som har fördelen att kontinuerligt samla in mätdata från mittpunkten av k-space, vilket resulterar i lägre rörelsekänslighet under bildtagningstillfället. Radiell sampling har dock begränsningar som måste tas i beaktande, som gradient imperfektioner och gradientfördröjningar. Dessa leder till förvrängningar i samplingspositioneringen i k-space, även känt som trajektoriefel, vilket ger upphov till artefakter vid bildrekonstruktion. Syftet med projektet är att korrigera för dessa trajektoriefel så att den rekonstruerade bilden innehåller färre artefakter. Olika metoder har föreslagits för korrektion av gradientfel. Metoden som användes i detta projekt baseras på gradient impulsresponsfunktionen (GIRF), som karaktäriserar gradient systemet. För att estimera de verkliga samplingspositionerna i k-space beräknades de förvrängda gradientvågformerna efter varje mätning. Detta gjordes genom att först modellera de nominella gradientvågformerna och därefter utföra en faltning med GIRF. De utförda experimenten under projektets gång bestod av bildtagning av två fantomer och ett antal in-vivo mätningar för att identifiera eventuella skillnader i de rekonstruerade bilderna. Pulssekvenserna som användes under projektet var FLASH och bSSFP. Resultaten visade att GIRF-korrektionen reducerade artefakter orsakade av gradient imperfektioner i de rekonstruerade bilderna tagna med FLASH-sekvensen. Erhållna resultat med bSSFP-sekvensen var å andra sidan inte lika lyckade på grund av inkomplett modellering av gradientvågformerna. Slutsatsen som kan dras är att GIRF-korrektionen kompenserar för trajektoriefel i radiell sampling för FLASH-sekvensen och ger rekonstruerade bilder där artefakterna nästan eliminerats. Ett förslag för framtida arbeten är att vidare undersöka modelleringen av bSSFP-sekvensen för att erhålla bättre bilder.

Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro / O presente trabalho aborda o problema de identificação de danos em uma estrutura a partir de sua resposta impulsiva. No modelo adotado, a integridade estrutural é continuamente descrita por um parâmetro de coesão. Sendo assim, o Modelo de Elementos Finitos (MEF) é utilizado para discretizar tanto o campo de deslocamentos, quanto o campo de coesão. O problema de identificação de danos é, então, definido como um problema de otimização, cujo objetivo é minimizar, em relação a um vetor de parâmetros nodais de coesão, um funcional definido a partir da diferença entre a resposta impulsiva experimental e a correspondente resposta prevista por um MEF da estrutura. A identificação de danos estruturais baseadas no domínio do tempo apresenta como vantagens a aplicabilidade em sistemas lineares e/ou com elevados níveis de amortecimento, além de apresentar uma elevada sensibilidade à presença de pequenos danos. Estudos numéricos foram realizados considerando-se um modelo de viga de Euler-Bernoulli simplesmente apoiada. Para a determinação do posicionamento ótimo do sensor de deslocamento e do número de pontos da resposta impulsiva, a serem utilizados no processo de identificação de danos, foi considerado o Projeto Ótimo de Experimentos. A posição do sensor e o número de pontos foram determinados segundo o critério D-ótimo. Outros critérios complementares foram também analisados. Uma análise da sensibilidade foi realizada com o intuito de identificar as regiões da estrutura onde a resposta é mais sensível à presença de um dano em um estágio inicial. Para a resolução do problema inverso de identificação de danos foram considerados os métodos de otimização Evolução Diferencial e Levenberg-Marquardt. Simulações numéricas, considerando-se dados corrompidos com ruído aditivo, foram realizadas com o intuito de avaliar a potencialidade da metodologia de identificação de danos, assim como a influência da posição do sensor e do número de dados considerados no processo de identificação. Com os resultados obtidos, percebe-se que o Projeto Ótimo de Experimentos é de fundamental importância para a identificação de danos. / The present work deals with the damage identification problem in mechanical structures from their impulse response. In the adopted model, the structural integrity is continually described by a cohesion parameter and the finite element model (FEM) is used to spatially discretize both the displacement and cohesion fields. The damage identification problem is then posed as an optimization one, whose objective is to minimize, with respect to the vector of nodal cohesion parameters, a functional based on the difference between the experimentally obtained impulse response and the corresponding one predicted by an FEM of the structure. The damage identification problem built on the time domain presents some advantages, as the applicability in linear systems with high levels of damping an/or closed spaced modes, and in nonlinear systems. Besides, the time domain approaches present high sensitivities to the presence of small damages. Numerical studies were carried out considering a simply supported Euler-Bernoulli beam. Optimal experiment design techniques were considered with the aim at determining the optimal position of the displacement sensor and also the number of points of the impulse response to be considered in the identification process. The Differential Evolution optimization method and the Levenberg-Marquardt method were considered to solve the inverse problem of damage identification. Numerical analysis were carried out in order to assess the influence, on the identification results, of noise in the synthetic experimental data, of the sensor position, and of the number of points retained in the impulse response. The presented results shown the potentiality of the proposed damage identification approach and also the importance of the optimal experiment design for the quality of the identification. al importance for the identification of damage.

Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro / O presente trabalho aborda o problema de identificação de danos em uma estrutura a partir de sua resposta impulsiva. No modelo adotado, a integridade estrutural é continuamente descrita por um parâmetro de coesão. Sendo assim, o Modelo de Elementos Finitos (MEF) é utilizado para discretizar tanto o campo de deslocamentos, quanto o campo de coesão. O problema de identificação de danos é, então, definido como um problema de otimização, cujo objetivo é minimizar, em relação a um vetor de parâmetros nodais de coesão, um funcional definido a partir da diferença entre a resposta impulsiva experimental e a correspondente resposta prevista por um MEF da estrutura. A identificação de danos estruturais baseadas no domínio do tempo apresenta como vantagens a aplicabilidade em sistemas lineares e/ou com elevados níveis de amortecimento, além de apresentar uma elevada sensibilidade à presença de pequenos danos. Estudos numéricos foram realizados considerando-se um modelo de viga de Euler-Bernoulli simplesmente apoiada. Para a determinação do posicionamento ótimo do sensor de deslocamento e do número de pontos da resposta impulsiva, a serem utilizados no processo de identificação de danos, foi considerado o Projeto Ótimo de Experimentos. A posição do sensor e o número de pontos foram determinados segundo o critério D-ótimo. Outros critérios complementares foram também analisados. Uma análise da sensibilidade foi realizada com o intuito de identificar as regiões da estrutura onde a resposta é mais sensível à presença de um dano em um estágio inicial. Para a resolução do problema inverso de identificação de danos foram considerados os métodos de otimização Evolução Diferencial e Levenberg-Marquardt. Simulações numéricas, considerando-se dados corrompidos com ruído aditivo, foram realizadas com o intuito de avaliar a potencialidade da metodologia de identificação de danos, assim como a influência da posição do sensor e do número de dados considerados no processo de identificação. Com os resultados obtidos, percebe-se que o Projeto Ótimo de Experimentos é de fundamental importância para a identificação de danos. / The present work deals with the damage identification problem in mechanical structures from their impulse response. In the adopted model, the structural integrity is continually described by a cohesion parameter and the finite element model (FEM) is used to spatially discretize both the displacement and cohesion fields. The damage identification problem is then posed as an optimization one, whose objective is to minimize, with respect to the vector of nodal cohesion parameters, a functional based on the difference between the experimentally obtained impulse response and the corresponding one predicted by an FEM of the structure. The damage identification problem built on the time domain presents some advantages, as the applicability in linear systems with high levels of damping an/or closed spaced modes, and in nonlinear systems. Besides, the time domain approaches present high sensitivities to the presence of small damages. Numerical studies were carried out considering a simply supported Euler-Bernoulli beam. Optimal experiment design techniques were considered with the aim at determining the optimal position of the displacement sensor and also the number of points of the impulse response to be considered in the identification process. The Differential Evolution optimization method and the Levenberg-Marquardt method were considered to solve the inverse problem of damage identification. Numerical analysis were carried out in order to assess the influence, on the identification results, of noise in the synthetic experimental data, of the sensor position, and of the number of points retained in the impulse response. The presented results shown the potentiality of the proposed damage identification approach and also the importance of the optimal experiment design for the quality of the identification. al importance for the identification of damage.

This thesis is composed of three chapters that propose some novel approaches on tail risk for financial market and forecasting in finance and macroeconomics. The first part of this dissertation focuses on financial market correlations and introduces a simple measure of tail correlation, TailCoR, while the second contribution addresses the issue of identification of non- normal structural shocks in Vector Autoregression which is common on finance. The third part belongs to the vast literature on predictions of economic growth; the problem is tackled using a Bayesian Dynamic Factor model to predict Norwegian GDP.Chapter I: TailCoRThe first chapter introduces a simple measure of tail correlation, TailCoR, which disentangles linear and non linear correlation. The aim is to capture all features of financial market co- movement when extreme events (i.e. financial crises) occur. Indeed, tail correlations may arise because asset prices are either linearly correlated (i.e. the Pearson correlations are different from zero) or non-linearly correlated, meaning that asset prices are dependent at the tail of the distribution.Since it is based on quantiles, TailCoR has three main advantages: i) it is not based on asymptotic arguments, ii) it is very general as it applies with no specific distributional assumption, and iii) it is simple to use. We show that TailCoR also disentangles easily between linear and non-linear correlations. The measure has been successfully tested on simulated data. Several extensions, useful for practitioners, are presented like downside and upside tail correlations.In our empirical analysis, we apply this measure to eight major US banks for the period 2003-2012. For comparison purposes, we compute the upper and lower exceedance correlations and the parametric and non-parametric tail dependence coefficients. On the overall sample, results show that both the linear and non-linear contributions are relevant. The results suggest that co-movement increases during the financial crisis because of both the linear and non- linear correlations. Furthermore, the increase of TailCoR at the end of 2012 is mostly driven by the non-linearity, reflecting the risks of tail events and their spillovers associated with the European sovereign debt crisis. Chapter II: On the identification of non-normal shocks in structural VARThe second chapter deals with the structural interpretation of the VAR using the statistical properties of the innovation terms. In general, financial markets are characterized by non- normal shocks. Under non-Gaussianity, we introduce a methodology based on the reduction of tail dependency to identify the non-normal structural shocks.Borrowing from statistics, the methodology can be summarized in two main steps: i) decor- relate the estimated residuals and ii) the uncorrelated residuals are rotated in order to get a vector of independent shocks using a tail dependency matrix. We do not label the shocks a priori, but post-estimate on the basis of economic judgement.Furthermore, we show how our approach allows to identify all the shocks using a Monte Carlo study. In some cases, the method can turn out to be more significant when the amount of tail events are relevant. Therefore, the frequency of the series and the degree of non-normality are relevant to achieve accurate identification.Finally, we apply our method to two different VAR, all estimated on US data: i) a monthly trivariate model which studies the effects of oil market shocks, and finally ii) a VAR that focuses on the interaction between monetary policy and the stock market. In the first case, we validate the results obtained in the economic literature. In the second case, we cannot confirm the validity of an identification scheme based on combination of short and long run restrictions which is used in part of the empirical literature.Chapter III :Nowcasting NorwayThe third chapter consists in predictions of Norwegian Mainland GDP. Policy institutions have to decide to set their policies without knowledge of the current economic conditions. We estimate a Bayesian dynamic factor model (BDFM) on a panel of macroeconomic variables (all followed by market operators) from 1990 until 2011.First, the BDFM is an extension to the Bayesian framework of the dynamic factor model (DFM). The difference is that, compared with a DFM, there is more dynamics in the BDFM introduced in order to accommodate the dynamic heterogeneity of different variables. How- ever, in order to introduce more dynamics, the BDFM requires to estimate a large number of parameters, which can easily lead to volatile predictions due to estimation uncertainty. This is why the model is estimated with Bayesian methods, which, by shrinking the factor model toward a simple naive prior model, are able to limit estimation uncertainty.The second aspect is the use of a small dataset. A common feature of the literature on DFM is the use of large datasets. However, there is a literature that has shown how, for the purpose of forecasting, DFMs can be estimated on a small number of appropriately selected variables.Finally, through a pseudo real-time exercise, we show that the BDFM performs well both in terms of point forecast, and in terms of density forecasts. Results indicate that our model outperforms standard univariate benchmark models, that it performs as well as the Bloomberg Survey, and that it outperforms the predictions published by the Norges Bank in its monetary policy report. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

Corruption is a social evil that resonates far and deep in societies, eroding trust in governance, weakening the rule of law, impairing economic development, and exacerbating poverty, social tension, and inequality. It is a multidimensional and complex societal malady that occurs in various forms and contexts. As such, any effort to combat corruption must be accompanied by a thorough examination of the attributes that might play a key role in exacerbating or mitigating corrupt environments. This dissertation identifies a number of attributes that influence corruption, using machine learning techniques, neural network analysis, and time series causal relationship analysis and aggregated data from 113 countries from 2007 to 2017. The results suggest that improvements in technological readiness, human development index, and e-governance index have the most profound impacts on corruption reduction. This dissertation discusses corruption at each phase of infrastructure systems development and engineering ethics that serve as a foundation for corruption mitigation. The dissertation then applies novel analytical efficiency measurement methods to measure infrastructure inefficiencies, and to rank infrastructure administrative jurisdictions at the state level. An efficiency frontier is developed using optimization and the highest performing jurisdictions are identified. The dissertation’s framework could serve as a starting point for governmental and non-governmental oversight agencies to study forms and contexts of corruption and inefficiencies, and to propose influential methods for reducing the instances. Moreover, the framework can help oversight agencies to promote the overall accountability of infrastructure agencies by establishing a clearer connection between infrastructure investment and performance, and by carrying out comparative assessments of infrastructure performance across the jurisdictions under their oversight or supervision.

[pt] Esta tese é composta por dois artigos. No primeiro, nós propomos e implementamos um novo índice de vulnerabilidade que é baseado em um VAR bayesiano, estrutural e que pode variar no tempo e junto com um hipótese de exogeneidade em bloco para um determinado par composto por uma economia grande e uma pequena economia aberta. O índice é baseado na soma das respostas da pequena economia aberta a choques na economia grande ao longo do tempo. Isso permite que nós consigamos distinguir e medir as fontes dos choques, varáveis impactadas e duração do co-movimento ou vulnerabilidade. Nosso índice sugere que a vulnerabilidade é determinada principalmente por características específicas de cada par de países, mas tendências mais gerais de descasamento podem ser observadas em um número considerável de pares de países, especialmente em janelas mais longas. Nós propomos uma aplicação desse método a um arcabouço de bancos globais - o que nos permite medir alguns mecanismos teóricos nunca antes medidos. Usando uma amostra de países desenvolvidos e em desenvolvimento, nós não econtramos evidência da prevalência de tais mecanismos sobre co-movimento. No segundo artigo, estudamos como incentivos fiscais impactam o comportamento da firma. Incentivos fiscais tem impacto sobre salários? Incentivos fiscais tem impacto sobre a composição da força de trabalho das firmas? E tem impacto sobre o tamanho dessas firmas? Para responder essas perguntas, combinamos a RAIS com uma nova base de dados - no nível da firma - de dois programas de incentivos fiscais do estado do Espírito Santo - Invest-ES e Compete-ES. Usando o estimador de diferenças em diferenças para efeitos de tratamento intertemporais, nós estumaos os impactos desses programas no nível municipal e, pela primeira vez, no nível das firmas. Nós não encontramos impacto significativo de de incentivos fiscais em quaisquer variáveis de nível municipal. No nível das firmas, por outro lado, nossos resultados indicam que há uma maior migração vinda de outros municípios do estado do Espírito Santo e também indicam que há um crescimento temporário no número de empregos nas firmas tratadas. Com relação a salário e níveis educacionais da força de trabalho, não foi documentado qualquer impacto significativo. / [en] This thesis is composed of two articles. In the first one, we propose and implement a new index of vulnerability which is based on a structural time-varying bayesian VAR with a block-exogeneity hypothesis for a given pair of a large economy and a small open economy. The index is based on the sum of the responses of the small open economy to shocks in the large economy over time, thus allowing us to disentangle and measure the source of the shock, impact variables and duration of the co-movement or vulnerability. Our index suggests that the business cycle co-movement is led primarily by country-pair characteristics, but decoupling trends can be observed in a considerable number of country-pairs, specially at long term windows. We provide an application of this approach to a global banks framework - which allows us to measure some yet unmeasured theoretical mechanisms. Using a sample of developed and developing countries, we find no evidence of the prevalence of such mechanisms in business cycle co-movement. In the second article, we study how tax incentives impact the firm s behavior and choices in the labor market. Do tax incentives affect wages? Do these incentives alter the composition of a firm s labor force? And what about its size? To answer these questions, we merge RAIS - a linked Brazilian employer-employee dataset - with a novel, firm-level, dataset on two fiscal incentives programs in the state of Espírito Santo - Invest-ES and Compete-ES. By using a differences-in-differences estimator of intertemporal treatment effects, we study the impacts of these programs on municipality-level and, for the first time, firm-level variables. We do not find statistically significant impact of fiscal incentives on any relevant municipality-level variables. On firm-level variables, however, our results point to higher migration from other municipalities in the state of Espírito Santo and temporary growth in the number of jobs. Regarding wages and educational levels of the labor force, no statistically significant impact was documented.