本論文由三篇文章所構成。第二章主要針對參考點（reference points）的選擇問題。在綠色國民所得帳中，選擇哪個參考點作為扣除環境質損與自然資源折耗是很重要的。由於既有文獻中的參考點選擇大多過於極端，本文遂針對文獻上的缺失，建議以法定的環保標準做為參考點，使得環境質損、環境效益、自然資源折耗與服務都可顯示出來。 第三章則以台灣的烏魚業為對象，以不合作賽局分析大陸漁民在烏魚迴游路徑上捕魚以及溫室效應對台灣漁業及消費者造成的影響。 第四章則指出Huang and Cai (1994)一文在理論模型設計上的問題，並提出改正，以使其論點獲得支持。 / This dissertation is made up of three essays that address the issues of green net national product, renewable resources and pollution externalities. In Chapter 2, the issue of choosing reference points is discussed. The reference point is a key parameter for estimating the values of environmental degradation and natural resource depletion that are used to adjust the Net National Product. Most of the reference points used in the literature are extreme points in the spectrum of environmental quality and may thus result in estimated values of degradation and depletion that are not consistent with the standard theory of environmental economics. In this chapter, we argue that by using clearly defined standards as reference points, e.g., environmental quality standards, for managing environmental quality and natural resources, correct values of environmental degradation and services, and natural resource depletion and direct nature services, as well as green NNP can emerge. In Chapter 3, we examine the problem of non-cooperative fishing between Mainland China (MC) and Taiwan (TW) as well as the effects of rising sea surface temperature (SST) on the grey mullet fishery. By setting the three stages of the non-cooperative game, it is shown that the expansion of the fleets in MC and the rising SST cause the rent obtained from the TW fishery to decline, and that Taiwan may partially offset such an adverse effect by adjusting its fleet size. This essay also shows that Taiwan can expand its fleet to a greater extent when facing an imperfectly elastic demand for fish than when facing a perfectly elastic demand. In addition, when consumer welfare is included in the determination of the size of the fleet, fleet size can expand more than when profit is considered alone. In Chapter 4, we point out the problems in Huang and Cai (1994) that arise from the absence of a “pollution externality.” We modify their model by introducing the emission tax and find that it supports Huang and Cai (1994)’s claim that the share of private abatement expenditure is constant at all times, a claim that is criticized by Shieh, et al. (2001).

This thesis, which consists of four chapters, focuses on forecasting in a data-rich environment and related computational issues. Chapter 1, “An embarrassment of riches: Forecasting using large panels” explores the idea of combining forecasts from various indicator models by using Bayesian model averaging (BMA) and compares the predictive performance of BMA with predictive performance of factor models. The combination of these two methods is also implemented, together with a benchmark, a simple autoregressive model. The forecast comparison is conducted in a pseudo out-of-sample framework for three distinct datasets measured at different frequencies. These include monthly and quarterly US datasets consisting of more than 140 predictors, and a quarterly Swedish dataset with 77 possible predictors. The results show that none of the considered methods is uniformly superior and that no method consistently outperforms or underperforms a simple autoregressive process. Chapter 2. “Forecast combination using predictive measures” proposes using out-of-sample predictive likelihood as the basis for BMA and forecast combination. In addition to its intuitive appeal, the use of the predictive likelihood relaxes the need to specify proper priors for the parameters of each model. We show that the forecast weights based on the predictive likelihood have desirable asymptotic properties. And that these weights will have better small sample properties than the traditional in-sample marginal likelihood when uninformative priors are used. In order to calculate the weights for the combined forecast, a number of observations, a hold-out sample, is needed. There is a trade off involved in the size of the hold-out sample. The number of observations available for estimation is reduced, which might have a detrimental effect. On the other hand, as the hold-out sample size increases, the predictive measure becomes more stable and this should improve performance. When there is a true model in the model set, the predictive likelihood will select the true model asymptotically, but the convergence to the true model is slower than for the marginal likelihood. It is this slower convergence, coupled with protection against overfitting, which is the reason the predictive likelihood performs better when the true model is not in the model set. In Chapter 3. “Forecasting GDP with factor models and Bayesian forecast combination” the predictive likelihood approach developed in the previous chapter is applied to forecasting GDP growth. The analysis is performed on quarterly economic dataset from six countries: Canada, Germany, Great Britain, Italy, Japan and United States. The forecast combination technique based on both in-sample and out-of-sample weights is compared to forecasts based on factor models. The traditional point forecast analysis is extended by considering confidence intervals. The results indicate that forecast combinations based on the predictive likelihood weights have better forecasting performance compared with the factor models and forecast combinations based on the traditional in-sample weights. In contrast to common findings, the predictive likelihood does improve upon an autoregressive process for longer horizons. The largest improvement over the in-sample weights is for small values of hold-out sample sizes, which provides protection against structural breaks at the end of the sample period. The potential benefits of model averaging as a tool for extracting the relevant information from a large set of predictor variables come at the cost of considerable computational complexity. To avoid evaluating all the models, several approaches have been developed to simulate from the posterior distributions. Markov chain Monte Carlo methods can be used to directly draw from the model posterior distributions. It is desirable that the chain moves well through the model space and takes draws from regions with high probabilities. Several computationally efficient sampling schemes, either one at a time or in blocks, have been proposed for speeding up convergence. There is a trade-off between local moves, which make use of the current parameter values to propose plausible values for model parameters, and more global transitions, which potentially allow faster exploration of the distribution of interest, but may be much harder to implement efficiently. Local model moves enable use of fast updating schemes, where it is unnecessary to completely reestimate the new, slightly modified, model to obtain an updated solution. The last fourth chapter “Computational efficiency in Bayesian model and variable selection” investigates the possibility of increasing computational efficiency by using alternative algorithms to obtain estimates of model parameters as well as keeping track of their numerical accuracy. Also, various samplers that explore the model space are presented and compared based on the output of the Markov chain. / Diss. Stockholm : Handelshögskolan, 2006

Zugl.: Köln, Univ., Diss., 2008 / Hergestellt on demand

The overview of solved problems is focused on human resources and education. The role of education in regional development, the role of human resources in economic growth and the support of education through public expenditure programs and measure 1.1 of OP HRE are described. Methods of assessing the education and economic performance of the regions are characterized. The theoretical part of the thesis concludes the knowledge economy and education in the context of the problematic of Industry 4.0. The aim of the thesis is to evaluate the educational structure of the population of the Czech Republic and its links with the support of employee training in companies within the area of support 1.1 OP HRE in the period 2007 - 2013 and with the development of regions. Part of the thesis is to capture GDP in individual regions of the Czech Republic in 2005 - 2014. The theoretical work contribution reflects the analysis performed and confirms that the leading positions in the volume of depleted financial resources provided by the 1.1 OP HRE belongs to a structurally problematic and at the same time educationally weak regions. The practical benefits of this thesis are the use of knowledge in the area of corporate education and regional development in the programming period of 2014 - 2020.

This dissertation aims to evaluate the sustainability of the industrial growth of the states of the Northeast from 1996 to 2003. To do so, it uses the methodology of the Green GDP, through the equation Industrial Green GDP = Gross Industrial Product (Depletion of Mineral Resources + Environment Degradation Costs), where depletion of mineral resources corresponds to the mineral extraction and the environment degradation costs are generated according to the proxies of the IPPS the Industrial Pollution Projection System. The behavior of the series leads to the possibility of a combination of growth of the industrial product vis-à-vis a contraction of the industrial green GDP, resulting in a paradox of growth. It is also generated an index of sustainability of the industrial activity, through the following relation: Gross Industrial Produtc-Industrial Green GDP/Gross Industrial Product, where the larger the gap between the gross industrial product and the green GDP, weaker is the sustainability. The research concludes that for the Northeast, the industrial economic activity is losing sustainability at 50% of its product. / Fundação de Amparo a Pesquisa do Estado de Alagoas / Esta dissertação tem como objetivo avaliar a sustentabilidade do crescimento econômico industrial dos Estados do Nordeste no período de 1996 a 2003. Para tanto, se utiliza da metodologia do PIB Verde, adaptando-a para o produto industrial, obtido através do VBTI, o valor bruto da produção industrial, através da equação PIB Verde Industrial = VBTI (Depleção dos Recursos Minerais + Custos de Degradação Ambiental), onde a depleção dos recursos minerais corresponde ao valor da extração de bens minerais e os custos de degradação ambiental, obtidos através das proxies da metodologia do IPPS, Industrial Pollution Projection System, aplicados diretamente sobre o produto industrial setorial (que compõe a PIA-IBGE, Pesquisa Industrial Anual do Instituto Brasileiro de Geografia e Estatística), gerando assim uma estimativa de custo por cada setor industrial. Ao se observar o comportamento da séria, conclui-se pela possibilidade de combinação de crescimento do PIB industrial vis-à-vis um decréscimo do PIB Verde industrial, conduzindo a um paradoxo de crescimento. Gera-se também um índice de sustentabilidade da atividade industrial (ISAI), obtido pela seguinte relação: ISAI= VBTI-PIB Verde Industrial/VBTI, onde quanto maior for a distância do PIB Verde em relação ao VBTI, menos sustentável é a atividade econômica. A pesquisa conclui que para o Nordeste como um todo, a atividade industrial compromete a sustentabilidade de seu produto em um patamar de 50%.

Considerando a importância de uma medida de alta frequência para o PIB do Rio Grande do Sul, o principal indicador de atividade econômica do estado, este trabalho foi dividido em três objetivos. O primeiro foi a estimação de uma série com frequência mensal para o PIB real do Rio Grande do Sul entre janeiro de 2002 e março de 2017, dado que o mesmo só é contabilizado em frequência trimestral. Para tanto, foi utilizado um modelo em espaço de estados que permite a estimação e nowcast do PIB mensal, utilizando séries coincidentes como fonte de informação para a interpolação dos dados trimestrais do PIB, em linha com Bernanke, Gertler e Watson (1997), Mönch e Uhlig (2005) e Issler e Notini (2016). O segundo objetivo foi comparar a série estimada com um indicador de atividade calculado pelo Banco Central do Brasil para o estado, o Índice de Atividade Econômica Regional (IBCR-RS), tanto em termos metodológicos como na capacidade em antecipar as variações do PIB trimestral antes de sua divulgação (nowcasting). O terceiro objetivo foi estabelecer a cronologia dos ciclos de expansão e recessão da economia gaúcha com o uso do algoritmo de Bry e Boschan (1971). Após a etapa de seleção das séries coincidentes e da estimação de diversos modelos de interpolação, foi escolhido para gerar a série mensal do PIB o modelo que utiliza somente a produção industrial como variável auxiliar, tendo este apresentado o melhor ajuste. A comparação do PIB mensal interpolado com o IBCR-RS mostrou que, além da vantagem computacional a favor do método proposto neste trabalho, a imposição da disciplina de que as variações do PIB mensal estimado devem ser exatamente iguais às do PIB trimestral faz com que a dinâmica de curto e longo prazo das variáveis sejam idênticas, o que não ocorre com o IBCR-RS. A cronologia dos pontos de inflexão da atividade econômica apontou três períodos recessivos na economia gaúcha desde janeiro de 2002: jun/2003 a abr/2005 (23 meses e queda acumulada de 8,79%); abr/2011 a abr/2012 (13 meses e queda acumulada de 9,47%); e jun/2013 a nov/2016 (42 meses e queda acumulada de 10,41%), sendo o encerramento deste último apontado somente com a inclusão dos resultados estimados pelo modelo para o segundo trimestre de 2017. Finalmente, os resultados do exercício de nowcasting do PIB mostraram desempenho superior do método proposto frente ao IBCR-RS em termos de antecipação do resultado do PIB de um trimestre a frente, tomando como base as medidas de MAE (erro absoluto médio, em inglês) e MSE (erro quadrático médio, em inglês), comumente usadas nesse intuito. / Giving the importance of a high frequency measure for Rio Grande do Sul’s GDP, the main indicator of economic activity of the state, this work was divided into three objectives. The first one was the estimation of monthly frequency series for Rio Grande do Sul’s real GDP between January/2002 and March/2017, since it is only accounted in quarterly basis. Therefore, we used a State-Space model that enables to estimate and nowcast the monthly GDP, using coincident series as a source of information for the interpolation of quarterly GDP data, in line with Bernanke, Gertler e Watson (1997), Mönch e Uhlig (2005) and Issler e Notini (2016). The second objective was to compare the estimated series with an activity indicator calculated by the Central Bank of Brazil for the state, the Regional Economic Activity Index (IBCR-RS), both in methodological terms and in the capability to anticipate the quarterly GDP release (nowcasting). The third objective was to establish the chronology of the cycles of expansion and recession of the economy of Rio Grande do Sul using the algorithm of Bry e Boschan (1971). After the selection of the coincident series and the estimation of several interpolation models, the chosen model to generate the monthly GDP series uses only the industrial production as an auxiliary variable, and this one presented the best fit. The comparison of the monthly GDP interpolated with the IBCR-RS showed that, in addition to the computational advantage in favor of the method proposed in this work, the imposition of the discipline that the estimated monthly GDP changes must be exactly the same as the quarterly GDP makes the short-term and long-term dynamics of the variables are identical, which is not the case with IBCR-RS. The chronology of the turning points of the economic activity pointed to three recessive periods in the economy of Rio Grande do Sul since January 2002: June/2003 to April/2005 (23 months and accumulated drop of 8.79%); April/2011 to April/2012 (13 months and accumulated fall of 9.47%); and June/2013 to November/2016 (42 months and 10.41% accumulated decrease), with the latter one closing only with the inclusion of the results estimated by the model for the second quarter of 2017. Finally, results for GDP’s nowcasting showed superior performance of the proposed method compared to the IBCR-RS in terms of anticipating quarter-to-quarter GDP results, based on the measures of MAE (absolute mean error) and MSE (mean square error), commonly used for this purpose.

The objective of this thesis is to describe the role of productivity in the economic development states of EU. Work is also trying to discover what factors have an impact on productivity. In this work was evaluated labor productivity, capital producitvity and total factor producitivty. The thesis contains models that consider human capital. It is also used linear regression and dynamic parameters.

This thesis deals with current issues relating to the mortgage market. The aim of the thesis is to analyse factors which influence the mortgage market in the Czech Republic and to describe their influence on the quantities of mortgages within last 10 years (2002-2011). The theoretical part introduces basic concepts related to the mortgage market, the legislation and chosen factors that could have an influence on the quantities of mortgages. Among these factors were chosen GDP, the rate of unemployment, the inflation rate and the interest rate of mortgage loans. One part deals with mortgage crises which came into existence in the USA in 2007 and had an impact on economics of other countries. In practical part there is an introduction into development of chosen factors and then there is finding out whether these factors influence the quantity of mortgages.

Este trabalho tem como objetivos analisar as semelhanças das respostas dos países da Zona do Euro aos choques na política monetária e no câmbio (identificados através de restrições de sinais nas funções impulso-resposta) e investigar a simetria das flutuações na taxa de crescimento do nível de atividade na região através da análise da importância relativa da resposta do crescimento do PIB destes países aos choques comum e específico identificados pelo modelo FAVAR utilizado, que foi estimado através de um método Bayesiano desenvolvido para incorporar prioris de Litterman (1986). A importância do choque comum (relativamente ao específico) nos diversos países, fornece uma medida do grau de integração dos diversos membros da Zona do Euro. O trabalho contribui para a análise do grau de integração dos países da Zona do Euro ao utilizar uma metodologia que permite o uso de um amplo conjunto de variáveis e ao identificar o grau de simetria das flutuações na taxa de crescimento do nível de atividade dos membros da região através da identificação dos choques comuns e específicos. Foram utilizados dados trimestrais de 1999.I a 2013.I para os 17 países da região. Os resultados encontrados apontam para a existência de uma maior integração entre as grandes economias da Zona do Euro ( com exceção da França) e uma integração menor para as menores economias (com exceção da Finlândia). / This dissertation aims at analyzing the similarities of the responses of countries in the Eurozone to shocks in the monetary policy and exchange rate policy (identified through restrictions on the signs in the impulse-response functions) and to investigate the symmetry of fluctuations in the rate of growth of the level of activity in the region by analyzing the relative importance of the response of GDP growth in these countries to common and specific shocks identified by the FAVAR model used in the paper, which was estimated using a Bayesian method developed to incorporate priors of Litterman (1986). The importance of the common shocks (in the particular) in many countries provides a measure of the degree of integration of several members of the Eurozone. The dissertation contributes to the analysis of the degree of integration of the countries of the Eurozone by using a methodology that allows the use of a big set of variables and to identify the degree of symmetry of the fluctuations in the growth rate of the activity level of members region through the identification of common and specific shocks. Using quarterly data from 1999.I to 2013.I for 17 countries in the region, the results show the existence of greater integration among the major economies of the Eurozone (except France) and lesser integration among the small economies (with the exception of Finland).

Considerando a importância de uma medida de alta frequência para o PIB do Rio Grande do Sul, o principal indicador de atividade econômica do estado, este trabalho foi dividido em três objetivos. O primeiro foi a estimação de uma série com frequência mensal para o PIB real do Rio Grande do Sul entre janeiro de 2002 e março de 2017, dado que o mesmo só é contabilizado em frequência trimestral. Para tanto, foi utilizado um modelo em espaço de estados que permite a estimação e nowcast do PIB mensal, utilizando séries coincidentes como fonte de informação para a interpolação dos dados trimestrais do PIB, em linha com Bernanke, Gertler e Watson (1997), Mönch e Uhlig (2005) e Issler e Notini (2016). O segundo objetivo foi comparar a série estimada com um indicador de atividade calculado pelo Banco Central do Brasil para o estado, o Índice de Atividade Econômica Regional (IBCR-RS), tanto em termos metodológicos como na capacidade em antecipar as variações do PIB trimestral antes de sua divulgação (nowcasting). O terceiro objetivo foi estabelecer a cronologia dos ciclos de expansão e recessão da economia gaúcha com o uso do algoritmo de Bry e Boschan (1971). Após a etapa de seleção das séries coincidentes e da estimação de diversos modelos de interpolação, foi escolhido para gerar a série mensal do PIB o modelo que utiliza somente a produção industrial como variável auxiliar, tendo este apresentado o melhor ajuste. A comparação do PIB mensal interpolado com o IBCR-RS mostrou que, além da vantagem computacional a favor do método proposto neste trabalho, a imposição da disciplina de que as variações do PIB mensal estimado devem ser exatamente iguais às do PIB trimestral faz com que a dinâmica de curto e longo prazo das variáveis sejam idênticas, o que não ocorre com o IBCR-RS. A cronologia dos pontos de inflexão da atividade econômica apontou três períodos recessivos na economia gaúcha desde janeiro de 2002: jun/2003 a abr/2005 (23 meses e queda acumulada de 8,79%); abr/2011 a abr/2012 (13 meses e queda acumulada de 9,47%); e jun/2013 a nov/2016 (42 meses e queda acumulada de 10,41%), sendo o encerramento deste último apontado somente com a inclusão dos resultados estimados pelo modelo para o segundo trimestre de 2017. Finalmente, os resultados do exercício de nowcasting do PIB mostraram desempenho superior do método proposto frente ao IBCR-RS em termos de antecipação do resultado do PIB de um trimestre a frente, tomando como base as medidas de MAE (erro absoluto médio, em inglês) e MSE (erro quadrático médio, em inglês), comumente usadas nesse intuito. / Giving the importance of a high frequency measure for Rio Grande do Sul’s GDP, the main indicator of economic activity of the state, this work was divided into three objectives. The first one was the estimation of monthly frequency series for Rio Grande do Sul’s real GDP between January/2002 and March/2017, since it is only accounted in quarterly basis. Therefore, we used a State-Space model that enables to estimate and nowcast the monthly GDP, using coincident series as a source of information for the interpolation of quarterly GDP data, in line with Bernanke, Gertler e Watson (1997), Mönch e Uhlig (2005) and Issler e Notini (2016). The second objective was to compare the estimated series with an activity indicator calculated by the Central Bank of Brazil for the state, the Regional Economic Activity Index (IBCR-RS), both in methodological terms and in the capability to anticipate the quarterly GDP release (nowcasting). The third objective was to establish the chronology of the cycles of expansion and recession of the economy of Rio Grande do Sul using the algorithm of Bry e Boschan (1971). After the selection of the coincident series and the estimation of several interpolation models, the chosen model to generate the monthly GDP series uses only the industrial production as an auxiliary variable, and this one presented the best fit. The comparison of the monthly GDP interpolated with the IBCR-RS showed that, in addition to the computational advantage in favor of the method proposed in this work, the imposition of the discipline that the estimated monthly GDP changes must be exactly the same as the quarterly GDP makes the short-term and long-term dynamics of the variables are identical, which is not the case with IBCR-RS. The chronology of the turning points of the economic activity pointed to three recessive periods in the economy of Rio Grande do Sul since January 2002: June/2003 to April/2005 (23 months and accumulated drop of 8.79%); April/2011 to April/2012 (13 months and accumulated fall of 9.47%); and June/2013 to November/2016 (42 months and 10.41% accumulated decrease), with the latter one closing only with the inclusion of the results estimated by the model for the second quarter of 2017. Finally, results for GDP’s nowcasting showed superior performance of the proposed method compared to the IBCR-RS in terms of anticipating quarter-to-quarter GDP results, based on the measures of MAE (absolute mean error) and MSE (mean square error), commonly used for this purpose.