A dissertação traz um breve resumo da história da siderurgia no Brasil, e comentários sobre o que vem ocorrendo no setor no mundo. A dissertação busca contribuir com a estimação de demanda para mercados oligopolizados, utilizando como proxie o mercado siderúrgico brasileiro. O objetivo, portanto, é estimar as variáveis econômicas que impactam o consumo de vergalhão no Brasil. Para isso são criados diversos modelos econométricos de demanda, utilizando as modelagens VAR (Vetor Auto-regressivo), BVAR (Vetor Autoregressivo Bayesiano) e Variáveis Instrumentais (IV). A metodologia BVAR foi aquela que apresentou os melhores resultados, com os seus coeficientes sendo robustos e estatisticamente significantes, além de reproduzirem a teoria econômica. / The present dissertation begins with a small resume of the history of the steel sector in Brazil, added to this some comments about that in the world. The dissertation aims to contribute with the estimation of oligopoly markets demand, using as a proxy the steel industry in Brazil. The main objective is to estimate the economics variables witch impact the rebar consumption in Brazil. The estimates are made using different econometric methodologies as VAR (Vector Autoregressive), BVAR (Bayesian Vector Autoregressive) and Instrumental Variables. The BVAR methodology is the one witch presents the best results, with the coefficients signs being robust and statistic significant, besides reproducing the economic theory.

A dissertação traz um breve resumo da história da siderurgia no Brasil, e comentários sobre o que vem ocorrendo no setor no mundo. A dissertação busca contribuir com a estimação de demanda para mercados oligopolizados, utilizando como proxie o mercado siderúrgico brasileiro. O objetivo, portanto, é estimar as variáveis econômicas que impactam o consumo de vergalhão no Brasil. Para isso são criados diversos modelos econométricos de demanda, utilizando as modelagens VAR (Vetor Auto-regressivo), BVAR (Vetor Autoregressivo Bayesiano) e Variáveis Instrumentais (IV). A metodologia BVAR foi aquela que apresentou os melhores resultados, com os seus coeficientes sendo robustos e estatisticamente significantes, além de reproduzirem a teoria econômica. / The present dissertation begins with a small resume of the history of the steel sector in Brazil, added to this some comments about that in the world. The dissertation aims to contribute with the estimation of oligopoly markets demand, using as a proxy the steel industry in Brazil. The main objective is to estimate the economics variables witch impact the rebar consumption in Brazil. The estimates are made using different econometric methodologies as VAR (Vector Autoregressive), BVAR (Bayesian Vector Autoregressive) and Instrumental Variables. The BVAR methodology is the one witch presents the best results, with the coefficients signs being robust and statistic significant, besides reproducing the economic theory.

A dissertação traz um breve resumo da história da siderurgia no Brasil, e comentários sobre o que vem ocorrendo no setor no mundo. A dissertação busca contribuir com a estimação de demanda para mercados oligopolizados, utilizando como proxie o mercado siderúrgico brasileiro. O objetivo, portanto, é estimar as variáveis econômicas que impactam o consumo de vergalhão no Brasil. Para isso são criados diversos modelos econométricos de demanda, utilizando as modelagens VAR (Vetor Auto-regressivo), BVAR (Vetor Autoregressivo Bayesiano) e Variáveis Instrumentais (IV). A metodologia BVAR foi aquela que apresentou os melhores resultados, com os seus coeficientes sendo robustos e estatisticamente significantes, além de reproduzirem a teoria econômica. / The present dissertation begins with a small resume of the history of the steel sector in Brazil, added to this some comments about that in the world. The dissertation aims to contribute with the estimation of oligopoly markets demand, using as a proxy the steel industry in Brazil. The main objective is to estimate the economics variables witch impact the rebar consumption in Brazil. The estimates are made using different econometric methodologies as VAR (Vector Autoregressive), BVAR (Bayesian Vector Autoregressive) and Instrumental Variables. The BVAR methodology is the one witch presents the best results, with the coefficients signs being robust and statistic significant, besides reproducing the economic theory.

<p dir="ltr">Seafood plays a pivotal role in global economies, livelihoods, and nutritional security. However, climate change and global pandemics pose significant threats to seafood harvests, production, supply chains, and marketing channels. The focus of my thesis is to understand the impact of external factors on our seafood resources and explore adaptive strategies in the face of uncertainties. We utilize economics techniques to study human-nature systems by zooming into social elements (government agencies, industry stakeholders, and fish farmers/fishermen) and aquatic resources. The three essays of my thesis delve into this inquiry from the perspectives of government, industry, and market, accordingly.</p><p dir="ltr">The first chapter in my thesis, <i>Climate Change and Snow Crab Harvest - Applying Random Effect Estimators with Instrumental Variable</i>, estimates the snow crab harvest function with unbalanced panel data of eastern Bering Sea snow crab, Canadian snow crab, Japanese snow crab, and Barents Sea snow crab. Specifically, we analyze the relationship between snow crab biomass, stock, and catch. To address the endogeneity of stock in the harvest function, climate change indicators are selected as instrumental variables. We identify that the Arctic Sea ice extent is effective in addressing the endogeneity and the random effects instrumental variable model with error components two stage least squares estimator performs the best to control heterogeneity. We find that a 1% increase in snow crab fishing effort is associated with a 0.42% increase in snow crab harvest, and a 1% increase in snow crab stock causes a 0.98% increase in snow crab harvest. The reported estimates indicate a large stock-harvest elasticity and provide supporting evidence for government fishery agencies to prioritize stock enhancement in policy designs.</p><p dir="ltr">The second chapter, <i>Online Media Sentiment Analysis of Shrimp and Salmon in the United States</i>, employs online media analytics on shrimp and salmon in the US to provide insights into consumer perceptions and potential demand signals for seafood. Search hits and mentions are quantified for top sources, domains, and prevalent terms. In addition, sentiment drivers and sentiment values are identified and calculated using natural language processing tools. The results reveal that the occurrence of peak mentions does not necessarily coincide with the peak of net sentiment, and farmed seafood consistently exhibits lower net sentiments compared to their wild counterparts. Autoregressive modeling is conducted to predict the dynamics of seafood’s net sentiments. The regional analysis demonstrates that public attitudes toward both farmed shrimp and salmon in the East North Central region exhibit a more positive net sentiment, while the New England and Middle Atlantic regions tend to have a lower net sentiment for farmed shrimp and salmon, respectively. The fitted forecast model serves as a supplementary tool for industry stakeholders to quickly respond to future public perceptions. Regional statistics also help the seafood industry tailor business strategies to different regions.</p><p dir="ltr">In the third chapter, <i>Comparative Case Study of Small-Scale Fish Processing for Local Seafood Supply</i><i>,</i> we examine the feasibility of utilizing a shared-use commercial kitchen and on-farm kitchen to support small-scale local fish processing, which helps diversify marketing channels in the US Midwest and supply seafood to local food systems. A case study of each facility type is assessed for economic viability for fish farmers. The financial analysis suggests farmers interested in processing tilapia or rainbow trout from 2,500 lbs to 5,000 lbs per year utilize rental commercial kitchens. A minimum of 15% markup and processing of 10,000 lbs/year tilapia is required to make the on-farm kitchen option more viable. For farmers who process rainbow trout, 10,000 lbs/year with a 10% markup using an on-farm kitchen is a better choice. Factoring in the stochastic variability of raw product prices, rental rates, and set-up costs, we provide simulated ranges for economic metrics including profitability index, payback period, and net present values. The reports of estimated costs, revenues, and breakeven prices, provide fish farmers with suggested selling prices, kitchen choices, and production levels to achieve optimum profits under risks.</p>