Mestrado em Economia
URI Permanente para esta coleção
Nível: Mestrado Acadêmico
Ano de início: 1994
Conceito atual na CAPES: 4
Ato normativo:
Homologado pelo CNE (Portaria MEC nº 486, de 14/05/2020).
Publicação no DOU em 18/05/2020, seção 1, p. 93.
Parecer nº 839/2019 CNE/CES
Periodicidade de seleção: Semestral
Área(s) de concentração: Teoria Econômica
Url do curso: https://economia.ufes.br/pt-br/pos-graduacao/PPGEco/detalhes-do-curso?id=1432
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- ItemA produtividade total dos fatores e o mercado acionário : evidências para o mercado brasileiro(Universidade Federal do Espírito Santo, 2025-02-25) Lago, Jardel Nogueira Oliveira; Moreira, Ricardo Ramalhete ; https://orcid.org/0000-0002-1905-4872; http://lattes.cnpq.br/3263921271806291; https://orcid.org/0000-0001-5605-185X; http://lattes.cnpq.br/0969060931987690; Monte, Edson Zambon ; https://orcid.org/0000-0002-6878-5428; http://lattes.cnpq.br/5543595580825181; Santos, Daiane Rodrigues dos ; https://orcid.org/0000-0001-9215-2260; http://lattes.cnpq.br/6580851334706525This dissertation analyzes the relationship between Total Factor Productivity (TFP) and the Brazilian stock market, considering different segments represented by the indices IBOV, SMLL, and IDIV. Using an econometric approach with Vector Autoregression (VAR) and Vector Error Correction (VEC) models, the research captures the short- and long-term dynamics between the variables and identifies the impacts of TFP shocks on stock market performance. The results indicate that positive TFP shocks favorably influence stock indices, with heterogeneous responses depending on the characteristics of the sectors represented by each index. Additionally, stock indices have a positive and permanent impact on TFP in the long term, suggesting that the stock market may act as a productivity catalyst
- ItemImpacto do processo educacional da Ufes: uma avaliação do diferencial de salário dos seus egressos no mercado de trabalho formal(Universidade Federal do Espírito Santo, 2024-09-20) Pena, Amanda Carla Ramos; Hott, Henrique Augusto Campos Fernandez; Giuberti, Ana Carolina; https://orcid.org/0000-0001-6685-6272; Melo, Ana Paula; Piza, Caio Cícero de ToledWe examine the short-term impact on earnings in the formal labor market for students of the Federal University of Espírito Santo (UFES). Using a quasi-experimental approach, we identify the causal effects of enrolling at UFES on formal employment and wages. Taking advantage of the characteristics of the university entrance exam, we applied the Fuzzy Regression Discontinuity (RDD) model to estimate these effects. Although the overall results are inconclusive, there are indications of a positive effect on wages for women who opted for the Quota System. Additionally, for women in general, there is a positive effect on the probability of obtaining a degree and the likelihood of employment in the formal market. However, further research with a more extended timeframe and a larger sample will be necessary to reach more robust conclusions.
- ItemPagamentos por serviços ambientais impactam a cobertura florestal? : uma avaliação do Programa Reflorestar(Universidade Federal do Espírito Santo, 2025-01-31) Bucher, Isabela Passoni; Seixas, Renato Nunes de Lima ; https://orcid.org/0000-0002-0510-5181; http://lattes.cnpq.br/1824359260532530; https://orcid.org/0000-0002-9533-8498; http://lattes.cnpq.br/0049535044438155; Giuberti, Ana Carolina ; https://orcid.org/0000-0001-6685-6272; http://lattes.cnpq.br/7213083068331720; Chimeli, Ariaster Baumgratz ; https://orcid.org/0000-0002-4269-8924; http://lattes.cnpq.br/5577138712196307Studies on Payment for Environmental Services (PES) programs have expanded, yet a gap remains in the literature regarding their impact evaluation, especially in the Brazilian context. This paper aims to fill this gap by evaluating the Reflorestar program, implemented by the government of the state of Espírito Santo, estimating its impact on forest cover and land use. Using data from the Rural Environmental Registry and satellite imagery, we applied Mahalanobis distance matching and difference-in-differences methods, as well as doubly robust estimators (Sant’Anna and Zhao, 2020) to estimate the average treatment effect on the treated. The results indicate that properties participating in the Reflorestar program experienced a significant percentage increase in areas of vegetation cover categories such as Forest (13.1%) and Early Stage Native Forest (10.1%), and land use categories such as general crops (12.5%), banana plantations (5.2%), and papaya plantations (2.2%). Furthermore, the results pointed to a reduction of approximately 13% in areas allocated to pasture. This study enriches the understanding of PES programs in Brazil, offering a thorough analysis of the impact evaluation of the Reflorestar program, highlighting positive effects in specific land use categories, particularly those aimed at reforestation
- ItemUm estudo sobre a sustentabilidade financeira do programa seguro-desemprego no Brasil (2000-2022) à luz da interpretação pós-keynesiana(Universidade Federal do Espírito Santo, 2024-03-28) Araújo, Leina Iade; Salles, Alexandre Ottoni Teatini ; https://orcid.org/0000-0001-9074-2531 ; http://lattes.cnpq.br/1107306178088215 ; https://orcid.org/0000-0001-8190-8886 ; http://lattes.cnpq.br/5110133939259957 ; Moreira, Ricardo Ramalhete ; https://orcid.org/0000-0002-1905-4872 ; http://lattes.cnpq.br/3263921271806291 ; Santos, Julio Fernando Costa ; https://orcid.org/0000-0002-2695-3200 ; http://lattes.cnpq.br/2980036542780514The present dissertation aims to study the financial sustainability of the unemployment insurance program in Brazil based on its ability to adapt to the country's socioeconomic changes between the years 2000 and 2022. To analyze this topic, the theoretical approach of the Post-Keynesian School was chosen. In this way, the historical evolution of the unemployment insurance program in Brazil and its operation from 2000 to 2022 is analyzed. The core of this investigation starts from the increase in insurance expenses, the amount paid with the benefit between 2008 and 2015 increased by around 140%. This led to the investigation of the long-term relationship between revenues from the Workers' Support Fund (FAT), unemployment insurance expenses and macroeconomic variables GDP and the unemployment rate. To this end, the econometric methodology of a vector error correction model (VECM) was used. The results indicated that the variables included in the model play a significant role in explaining variations in FAT revenue in the long term. This empirical evidence strengthens post-Keynesian interpretations about the need for stability in employment policies and the economy as a whole for the sustainability of the social protection system
- ItemMétodos de Machine Learning para Reconciliação Ótima de Séries Temporais Hierárquicas e Agrupadas(Universidade Federal do Espírito Santo, 2024-02-29) Miranda, Alberson da Silva; Pereira, Guilherme Armando de Almeida Pereira; https://orcid.org/0000-0002-2833-1384; http://lattes.cnpq.br/5139328860920389; https://orcid.org/0000-0001-9252-4175; http://lattes.cnpq.br/8428012718249167; Monte, Edson Zambom; https://orcid.org/0000-0002-6878-5428; http://lattes.cnpq.br/5543595580825181; Oliveira, Fernando Luiz Cyrino; https://orcid.org/0000-0003-1870-9440; http://lattes.cnpq.br/0348074510343282In the last decade, hierarchical time series forecasting has experienced substantial growth, characterized by advancements that have significantly improved the accuracy of forecasting models. Recently, machine learning methods have been integrated into the literature on hierarchical time series as a new approach for forecasting reconciliation. This work builds upon these advancements by further exploring the potential of ML methods for optimizing the reconciliation of hierarchical and grouped time series. Moreover, the impact of various training set acquisition strategies, such as in-sample forecasts obtained through rolling origin forecasting, fitted values of reestimated models, and fitted values of base forecast models, as well as alternative crossvalidation strategies, was investigated. To evaluate the proposed methodology, two case studies were carried out. The first study focuses on the Brazilian financial sector, specifically forecasting loan and financing balances for the State Bank of Espírito Santo. The second study uses Australian domestic tourism datasets, which are frequently referenced in hierarchical time series literature. The proposed methodology was compared with traditional methods for forecasting reconciliation such as bottom-up, top-down and minimum trace. The results show that there is no unique method or strategy that consistently outperforms all others. Nonetheless, the appropriate combination of ML method and strategy can lead to up to a 93% improvement in accuracy compared to the best-performing analytical reconciliation method.