Abstract Paper aims To predict monthly corn, soybean, and sugar spot prices in Brazil using hybrid forecasting techniques. Originality This study combines the Singular Spectrum Analysis with different forecasting methods. Research method This paper presents a set of hybrid forecasting approaches combining Singular Spectrum Analysis (SSA) with different univariate time series methods, ranging from complex seasonality methods to machine learning and autoregressive models to predict monthly corn, soybean, and sugar spot prices in Brazil. We carry out a range of out-of-sample forecasting experiments and use a comprehensive set of forecast evaluation metrics. We contrast the performance of the proposed approaches with that of a range of benchmark models. Main findings The results show that the proposed hybrid models present better performances, with the hybrid SSA-neural network approach providing the most competitive results in our sample. Implications for theory and practice Forecasting agricultural prices is of paramount importance to assist producers, farmers, and the industry in decision-making processes. corn soybean techniques SSA (SSA outofsample sample metrics performances SSAneural neural producers farmers decisionmaking decision making processes

RESUMO Objetivo: Estimar as taxas de notificação de casos de doença pelo coronavírus 2019 (COVID-19) para o Brasil em geral e em todos os estados. Métodos: Estimamos o número real de casos de COVID-19 utilizando o número de óbitos notificados no Brasil e em cada estado e a proporção entre casos e letalidade, conforme a Organização Mundial da Saúde. A proporção entre casos e letalidade prevista para o Brasil foi também ajustada segundo a pirâmide de idade populacional. Assim, a taxa de notificações pode ser definida como o número de casos confirmados (informados pelo Ministério da Saúde) dividido pelo número de casos previstos (estimado a partir do número de óbitos). Resultados: A taxa de notificação de COVID-19 no Brasil foi estimada em 9,2% (IC95%: 8,8% - 9,5%), sendo que, em todos os estados, as taxas encontradas foram inferiores a 30%. São Paulo e Rio de Janeiro, os estados mais populosos do país, mostraram baixas taxas de notificação (8,9% e 7,2%, respectivamente). A taxa de notificação mais alta ocorreu em Roraima (31,7%) e a mais baixa na Paraíba (3,4%). Conclusão: Os resultados indicam que a notificação de casos confirmados no Brasil é muito abaixo da encontrada em outros países que avaliamos. Assim, os responsáveis pela tomada de decisões, inclusive os governos, não têm conhecimento da real dimensão da pandemia, o que pode prejudicar a determinação das medidas de controle.

ABSTRACT Objective: To estimate the reporting rates of coronavirus disease 2019 (COVID-19) cases for Brazil as a whole and states. Methods: We estimated the actual number of COVID-19 cases using the reported number of deaths in Brazil and each state, and the expected case-fatality ratio from the World Health Organization. Brazil’s expected case-fatality ratio was also adjusted by the population’s age pyramid. Therefore, the notification rate can be defined as the number of confirmed cases (notified by the Ministry of Health) divided by the number of expected cases (estimated from the number of deaths). Results: The reporting rate for COVID-19 in Brazil was estimated at 9.2% (95%CI 8.8% - 9.5%), with all the states presenting rates below 30%. São Paulo and Rio de Janeiro, the most populated states in Brazil, showed small reporting rates (8.9% and 7.2%, respectively). The highest reporting rate occurred in Roraima (31.7%) and the lowest in Paraiba (3.4%). Conclusion: The results indicated that the reporting of confirmed cases in Brazil is much lower as compared to other countries we analyzed. Therefore, decision-makers, including the government, fail to know the actual dimension of the pandemic, which may interfere with the determination of control measures.

RESUMO Objetivo: Analisar as medidas adotadas por países que demonstraram controle sobre a transmissão da doença pelo novo coronavírus 2019 (COVID-19) e também como cada curva de casos acumulados se comportou após a implantação dessas medidas. Métodos: A metodologia adotada para este estudo compreendeu três fases: sistematização das medidas de controle adotadas por diferentes países, identificação dos pontos de inflexão na curva do crescimento do número de casos nesses países e análise específica dos dados brasileiros. Resultados: Observamos que China (excluindo-se Hubei), Hubei e Coreia do Sul foram eficazes na desaceleração das taxas de crescimento dos casos de COVID-19. A eficácia das medidas tomadas por esses países pode ser observada após 1 ou 2 semanas de sua aplicação. Na Itália e Espanha, foram tomadas medidas de controle em nível nacional em uma fase tardia da epidemia, o que pode ter contribuído para a elevada propagação da COVID-19. No Brasil, Rio de Janeiro e São Paulo adotaram medidas que podem ter sido eficazes na redução da rapidez da propagação do vírus, entretanto, só temos expectativa de ver seus efeitos no crescimento da curva nos próximos dias. Conclusão: Nossos resultados podem ajudar os responsáveis pela tomada de decisões em países em estágios relativamente precoces da epidemia, especialmente no Brasil, a compreenderem a importância das medidas de controle para desaceleração da curva de crescimento de casos confirmados.

ABSTRACT Objective: To analyse the measures adopted by countries that have shown control over the transmission of coronavirus disease 2019 (COVID-19) and how each curve of accumulated cases behaved after the implementation of those measures. Methods: The methodology adopted for this study comprises three phases: systemizing control measures adopted by different countries, identifying structural breaks in the growth of the number of cases for those countries, and analyzing Brazilian data in particular. Results: We noted that China (excluding Hubei Province), Hubei Province, and South Korea have been effective in their deceleration of the growth rates of COVID-19 cases. The effectiveness of the measures taken by these countries could be seen after 1 to 2 weeks of their application. In Italy and Spain, control measures at the national level were taken at a late stage of the epidemic, which could have contributed to the high propagation of COVID-19. In Brazil, Rio de Janeiro and São Paulo adopted measures that could be effective in slowing the propagation of the virus. However, we only expect to see their effects on the growth of the curve in the coming days. Conclusion: Our results may help decisionmakers in countries in relatively early stages of the epidemic, especially Brazil, understand the importance of control measures in decelerating the growth curve of confirmed cases.

ABSTRACT The Brazilian electricity energy matrix is essentially formed by hydraulic sources which currently account for 70% of the installed capacity. One of the most important characteristics of a generation system with hydro predominance is the strong dependence on the inflow regimes. Nowadays, the Brazilian power sector uses the PAR(p) model to generate scenarios for hydrological inflows. This approach does not consider any exogenous information that may affect hydrological regimes. The main objective of this paper is to infer on the influence of climatic events in water inflows as a way to improve the model’s performance. The proposed model is called “causal PAR(p)” and considers exogenous variables, such as El Niño and Sunspots, to generate scenarios for some Brazilian reservoirs. The result shows that the error measures decrease approximately 3%. This improvement indicates that the inclusion of climate variables to model and simulate the inflows time series is a valid exercise and should be taken into consideration.