Submitted by Marilene Donadel (marilene.donadel@unioeste.br) on 2019-03-22T22:55:03Z No. of bitstreams: 1 Daniel_Centuriao_2018.pdf: 4691006 bytes, checksum: dfa8ad2e90bc4e251943d5f57fc3dbb5 (MD5) / Made available in DSpace on 2019-03-22T22:55:03Z (GMT). No. of bitstreams: 1 Daniel_Centuriao_2018.pdf: 4691006 bytes, checksum: dfa8ad2e90bc4e251943d5f57fc3dbb5 (MD5) Previous issue date: 2018-03-02 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This study seeks to make some contributions to the debate on Brazilian deindustrialization, with a specific look at the determinants of changes in value added and employment in industry, and the structural factors that influenced these variations and the value of industrial production, for the period of 1990 to 2014. In order to contribute empirically, we used an analysis from the VEC (Vector Errors Correction) econometric model and an application of the input-output analysis with the SDA (Structural Decomposition Analysis) technique, in addition to a vast empirical review and theoretical history, in order to connect the results to the historical facts verified. In general, a period with a vast literature, with great changes of a political character, changes in the conduct of economic policy and, above all, in the connection of the Brazilian productive structure with the rest of the world. A major limitation was the availability of continuous data sets for key variables of the desired analysis. In addition to the data, the great volume and speed of events of the period that generate a certain limitation in the empirical evidence, since it is not always possible to construct suitable models to capture such effects. The results showed that industrial employment is significantly determined by productivity and by remuneration and value added by three groups of variables, one of macroeconomic character, one of foreign trade and another one related to the variations of value added of the other sectors of the economy. It was also found that structural changes, mainly in the direct coefficients of employed and value-added personnel, in technology and in demand, were decisive for the structural variations in occupation and added volume of industry in the period.. / Este estudo busca dar algumas contribuições ao debate sobre a desindustrialização brasileira, com um olhar específico para os determinantes das variações do valor adicionado e do emprego na indústria, e dos fatores estruturais que influenciaram estas variações e do valor da produção industrial, para o período de 1990 a 2014. Com o intuito de contribuir de forma empírica foram utilizados uma análise a partir do modelo econométrico VEC (Vector Erros Correction) e uma aplicação da análise de insumo-produto com a técnica SDA (Structural Decomposition Analysis), além de uma vasta revisão empírica e histórico teórica, a fim de conectar os resultados aos fatos históricos verificados. De modo geral se constitui um período com vasta literatura, com grandes mudanças de caráter político, de mudanças na condução da política econômica e principalmente de conexão da estrutura produtiva brasileira com o restante do mundo. Uma grande limitação verificada foi a disponibilidade de séries contínuas de dados para variáveis chave da análise desejada. Para além dos dados o grande volume e velocidade de acontecimentos do período que geram certa limitação na evidência empírica, pois nem sempre é possível construir modelos aptos captar tais efeitos. Os resultados demosntraram que o emprego industrial é significativamente determiando pela produtividade e pela remuneração e o valor adicionado por três grupos de variáveis, um de caráter macroeconômico, um de comércio exterior e outro referente as variações do valor adicionado dos demais setores da economia. Constatou-se também que modificações estruturais, principalemnet nos coefeicientes diretos do pessoal ocupado e do valor adicionado, na tecnologia e na demanda foram determinantes para as variações estruturais da ocupação e do vlor adicionado da indústria no período.

This thesis aims to produce short-term forecasts for the economic activity in Turkey. As a proxy for the economic activity, industrial production index is used. Univariate autoregressive distributed lag (ADL) models, vector autoregressive (VAR) models and combination forecasts method are utilized in a pseudo out-of-sample forecasting framework to obtain one-month ahead forecasts. To evaluate the models&rsquo / forecasting performances, the relative root mean square forecast error (RRMSFE) is calculated. Overall, results indicate that combining the VAR models with four endogenous variables yields the most substantial improvement in forecasting performance, relative to benchmark autoregressive (AR) model.

The dissertation is mainly made up of three empirical theses on the Optimum Currency Area theory, business cycle synchronization, and intra-industry trade. The second chapter conducts an empirical test into the theory of Optimum Currency Area. I investigate the feasibility of creating a currency union in East Asia by examining the dominance and symmetry of macroeconomic shocks. Relying on a series of structural Vector Autoregressive models with long-run and block exogeneity restrictions, I identify a variety of macroeconomic disturbances in eleven East Asian economies. To examine the nature of the disturbances, I look into the forecast error variance decomposition, correlation of disturbances, size of shocks, and speed of adjustments. Based on both statistical analysis and economic comparison, it is found that two groups of economies are subject to dominant and symmetrical domestic supply shocks, and that the two groups respond quickly to moderate-sized shocks. Therefore, it is economically feasible for the two groups of economies to foster common currency zones. The third chapter investigates the different effects of intra- and inter-industry trade on business cycle synchronization, controlling for financial market linkage and monetary policy making. The chapter is the first attempt to use intra- and inter-industry trade simultaneously in Instrument Variable estimations. The evidence in my paper is supportive that intra-industry trade increases business cycle synchronization, while inter-industry trade brings about divergence of cycles. The findings imply that country pairs with higher intra-industry trade intensity are more likely to experience synchronized business cycles and are more feasible to join a monetary union. My results also show that financial integration and monetary policy coordination provide no explanation for synchronization when industry-level trade are accounted for. The fourth chapter extends the third chapter and explores how the characteristics of global trade network influence intra-industry trade. Borrowing the concept of structural equivalence, the similarity of two countries’ aggregate trade relations with other countries, from the social network analysis, this study incorporates this measure of trade network to the augmented gravity model of intra-industry trade. I build up two fixed effects models to analyze intra-industry trade in the raw material and final product sectors among 182 countries from 1962 through 2000. Structural equivalence promotes intra-industry trade flows in the final product sector, but it does not influence intra-industry trade in the crude material sector. Moreover, structural equivalence has been increasingly important in boosting intra-industry trade over time. / Graduate / 0508

This paper proposes for the purposes of freight generation a spatial autoregressive model framework, combined with non-linear semi-parametric techniques. We demonstrate the capabilities of the model in a series of Monte Carlo studies. Moreover, evidence is provided for non-linearities in freight generation, through an applied analysis of European NUTS-2 regions. We provide evidence for significant spatial dependence and for significant non-linearities related to employment rates in manufacturing and infrastructure capabilities in regions. The non-linear impacts are the most significant in the agricultural freight generation sector.

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Investing in real estate carries inherent risks due to fluctuations in economic activity, changes in population dynamics, and shifts in market demand. While traditional approaches to scenario analysis, grounded in market expertise and keen intuition, have stood the test of time, they are also subjective and prone to human error and external influences. Therefore, an objective approach based on statistical inference was sought to serve as a supplementary instrument for real estate industry professionals. With efficacy and practical functionality in consideration, this thesis explores various solutions and determines autoregressive processes as a prime candidate for such an instrument. An instructive procedure is developed and applied to two data sets of historical Stockholm office rents and yields respectively. Starting with data typically available to real estate investors and advisors, this procedure implements locally weighted scatterplot smoothing, polynomial regression, autoregressive integrated moving average processes and matrix transformations to derive forecast intervals which may be applied to prescribe probability to precise ranges or points of the users variable of choice, several quarters into the future. The results demonstrate limitations in the distance of forecasting using this procedure but display satisfactory performance in the short to medium term. Additionally, the practical applicability of the procedure is reflected upon. / Investering i fastigheter medför inneboende risker på grund av fluktuationer i ekonomisk aktivitet, förändringar i befolkningsdynamik och efterfrågan på marknaden. Medan traditionella tillvägagångssätt för scenarioanalys, grundade på marknadsexpertis och skarp intuition, har bestått tidens tand, är de också subjektiva och medför risk för mänskliga fel och externa faktorer. Därav eftertraktades en objektiv metod baserad på statistiska processer för att fungera som ett kompletterande verktyg i fastighetsbranschen. Med hänsyn till effektivitet och praktisk funktionalitet fastställs autoregressiva processer som en primär kandidat som ett sådant verktyg i denna studie. En instruktiv procedur utvecklas och tillämpas på två dataset av historiska hyror respektive avkastning för kontorslokaler i Stockholm. Med utgångspunkt i data vanligt tillgänglig för fastighetsinvesterare och rådgivare implementerar denna procedur lokalt viktad spridningsdiagramsutjämning, polynomregression, autoregressiva integrerade glidande medelvärdesprocesser och matristransformationer för att härleda prognosintervall som kan användas för att föreskriva sannolikheter till exakta intervall eller punkter för variabeln i fråga, flera kvartal in i framtiden. Resultaten visar begränsningar i avståndet för prognoser med denna procedur men tillfredsställande prestanda på kort- till medellång sikt. Dessutom görs reflektioner kring den praktiska användbarheten av proceduren.

Orientador: Samuel da Silva / Resumo: O principal objetivo deste trabalho é usar métodos de extrapolação em coeficientes de modelos autorregressivos (AR), para fornecer informações futuras de condições de estruturas na existência de mecanismo de danos pré-definidos. Os modelos AR são estimados considerando a predição de um passo à frente, verificados e validados a partir de dados de vibração de uma estrutura na condição não danificada. Os erros de predição são usados para extrair um indicador para classificar a condição do sistema. Então, um novo modelo é identificado se qualquer variação de índices de dano ocorrer, e seus coeficientes são comparados com os do modelo de referência. A extrapolação dos coeficientes de AR é realizada através das splines cúbicas por partes que evitam possíveis instabilidades e alterações indesejáveis dos polinômios, obtendo aproximações adequadas através de polinômios de baixa ordem. Uma curva de tendência para o indicador capaz de predizer o comportamento futuro pode ser obtida a partir da extrapolação direta dos coeficientes. Uma estrutura de três andares com um para-choque e uma coluna de alumínio colocada no centro do último andar são analisados com diferentes cenários de dano para ilustrar a abordagem. Os resultados indicam a possibilidade de estimar a condição futura do sistema a partir dos dados de vibração nas condições de danos iniciais. / Abstract: The main purpose of this work is to apply extrapolation methods upon coefficients of autoregressive models (AR), to provide future condition information of structures in the existence of predefined damage mechanism. The AR models are estimated considering one-step-ahead prediction, verified and validated from vibration data of a structure in the undamaged condition. The prediction errors are used to extract an indicator to classify the system state condition. Then, a new model is identified if any variation of damage indices occurs, and its coefficients are compared to the ones from the reference model. The extrapolation of the AR coefficients is performed through the piecewise cubic splines that avoid possible instabilities and undesirable changes of the polynomials, obtaining suitable approximations through low-order polynomials. A trending curve for the indicator capable of predicting future behavior can be obtained from direct coefficient extrapolation. A benchmark of a three-story building structure with a bumper and an aluminum column placed on the center of the top floor is analyzed with different damage scenarios to illustrate the approach. The results indicate the feasibility of estimating the future system state from the vibration data in the initial damage conditions. / Mestre

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Στο πρώτο κεφάλαιο εισάγουμε τις βασικές έννοιες της διπλωματικής εργασίας. Αναφέρουμε τους ορισμούς και τον σκοπό της ανάλυσης χρονολογικών σειρών. Επίσης εισάγονται ορισμένα βασικά χαρακτηριστικά των χρονολογικών σειρών όπως η έννοια της στασιμότητας και της συνάρτησης αυτοσυσχέτισης και αναφέρουμε τις τρεις βασικές κατηγορίες στοχαστικών υποδειγμάτων χρονολογικών σειρών που αφορούν στις στάσιμες στοχαστικές διαδικασίες, οι οποίες θα αναλυθούν στα επόμενα κεφάλαια. Στο δεύτερο κεφάλαιο αναλύουμε τα αυτοπαλίνδρομα υποδείγματα, πρώτης, δεύτερης και γενικά p τάξης. Αναφέρονται παραδείγματα. Στο τρίτο κεφάλαιο αναλύουμε τα υποδείγματα κινητού μέσου πρώτης και γενικά q τάξης καθώς και μεικτά υποδείγματα πρώτης και γενικά (p,q) τάξης. Αναφέρονται παραδείγματα. Στο τέταρτο κεφάλαιο αναλύουμε χρονολογικές σειρές που δεν έχουν τα χαρακτηριστικά στάσιμων στοχαστικών διαδικασιών. Επίσης αναλύουμε την μεθοδολογία Box-Jenkins, η οποία είναι μία μέθοδος εξεύρεσης ενός στατιστικού υποδείγματος (ARIMA). Τέλος εφαρμόζεται η παραπάνω μέθοδος σε ένα παράδειγμα με τη χρήση του SPSS. / At the first chapter we introduce the basic concepts. We present the main definitions and the objectives of the time series analysis. Furthermore, we introduce some basic characteristics of the time series such as the concepts of “stationary process” and “autocorrelation”. Finally we mention three basic categories of time series models that concern stationary stochastic processes. Following in the second chapter we analyze the autoregressive models of first, second and generally “p” order. We present various relative examples. At the third chapter we analyze the moving average models of first and generally “q” order. Additionally, we analyze the mixed models of first and generally (p,q) order. Various relative examples are presented. Finally, at the forth chapter we analyze time series that don’t have the characteristics of stationary stochastic proceedings. Also we analyze the method Box-Jenkins. Furthermore, the later method is studied using the statistic software package SPSS.

[pt] Nessa dissertação seguimos o artigo de Evans e Marshall (1998) e propomos novas abordagens para modelar o desenvolvimento conjunto de variáveis macroeconômicas e retornos de títulos de renda fixacom diversas maturidades. Os modelos são estimados e comparados com outros, já tradicionais na literatura, baseados em modelos auto- regresivos univariados ou de correção de erros. em seguida, os novos modelos são utilizados para avaliar se a informação contida nas variáveis macroeconômicas e na estrutura a termo das taxas de juros ajuda a melhorar a capacidade de previsão. A principal conclusão é que, se o interese maior está em previsões de curto prazo, então não há melhoria significativa ao agregar outras informações que não sejam aquelas já contidas em observações passadas do próprio rendimento em questão. se, no entanto, o interesse maior está em previsões de longo prazo (que é o caso de fundos de previdência, sejam eles abertos ou fechados), então a informação inerente às variáveis macroeconômicas consegue melhorar o desempenho preditivo. / [en] In this dissertation we follow Evans and Marshall (1998) and propose new approaches for modeling the joint development of macro variables and the returns of government bond yields of several maturities. The models are estimated and compared with other forecasting schemes previously proposed in the literature, especially those relying on univariate, VAR and error correction methods. The models are then used to judge the hypothesis that the information content of macro variables and the term structure of interest rates as a whole helps improving forecasting performance. Our main conclusion is quite simple: if one is interested in computing short term forecasts, then there is no significant improvement in incorporating information other than the one already present in past observations of the yield at hand; however, if one worries about long term forecasts (which is frequently the case of pension insurance companies), then the information content of macro variables and the term structure can improve forecasting performance