Extreme temperatures are directly related to the occurrence of atmospheric extreme events, such as draughts, wildfires, and pollution level increases in urban areas. Policy makers, as well as society, can address such phenomenon by developing and applying methods which estimate and anticipate maximum temperature occurrences. In this research, we aim to develop a spatiotemporal model which analyzes maximum temperature trends values in the Indian 543 microregions between 1951 and 2020. In 27% of those, a maximum temperature above 45∘C was observed, at least in a year. Our analysis indicates further that 80% microregions have maximum temperatures above above 40∘C. Additionally, the results unveiled that East, Southwest, and Northwest microregions were the ones where the maximum temperatures had a higher increase with 2∘C being the average. The model developed is based on a Generalized Extreme Value (GEV) methodology, to estimate the maximum temperature values from 20 and 50 years. The projection for 20 years showed that in 15.83% of those microregions, at least one occurrence of a maximum temperature above 45∘C would occur; while in 50 years, it would happen in 21.54% of the microregions analyzed.
Há mais de 51 anos o mundo não vivia uma realidade tão assustadora e com um alto número de mortes, causadas por uma pandemia, como vivemos atualmente com a Covid-19. A Covid-19 surgiu na China em dezembro de 2019, quando houve relatos de uma infecção respiratória causada por um novo coronavírus, o SARS-CoV-2 (WU, Di; et al. 2020). A doença pode ser transmitida através do aperto de mão, gotículas de saliva e objetos ou superfícies contaminadas (Ministério da Saúde, 2020.). Como consequência de ser altamente contagiosa ocasionou o fechamento de lojas de serviços não essenciais, incluindo escolas e universidades. O propósito desse projeto é analisar os impactos causados pela Covid-19 na educação do Brasil, a fim de comparar as diferentes consequências na Rede Pública e Privada de ensino. Serão utilizadas técnicas de estatística descritiva para descrever e sumarizar os dados, obtidos através do Formulário que foi enviado para docentes e discentes, e testes de hipóteses para afirmar ou negar algumas hipóteses que serão levantadas ao longo deste artigo.
The level of the yield curve is strongly associated with a very important macroeconomic variable for developing economies: the inflation. Therefore, it becomes relevant for economic studies the development of a time series model that can accurately predict this variable. This article proposes the estimation and prediction of the yield curve level using the GAS (Generalized Autoregressive Score) class of time-varying coefficient models. The formulation of these models facilitates a general framework for time series modelling presenting a series of advantages, including the possibility of specifying any conditional distribution deemed appropriate for the yield curve level. In addition, the complete structure of the predictive distribution is transported to the mechanism that updates the time-varying parameters, via score function. When analyzing the evaluation criteria, the measures of adherence, and both Wilcoxon and Diebold & Mariano tests, it was verified that the adjustment of the GAS model (2,2) with gamma distribution to the series containing the Brazilian Yield Curve level of January 2006 and February 2017 presented a satisfactory result.
Widely used by economists in Brazil; the “Brazil Cost” concept refers to costs that hinder development, as they burden production, removing its competitive character, indispensable in a globalized economy. Brazil Cost may imply major obstacles to Foreign Direct Investment in the Country (FDI) and consequently impact the country's growth and development. The study evaluated the influence of variables that are part of the Brazil Cost in Foreign Direct Investment over the last six years. For this, the DMA -Dynamic Model Averaging methodology was used, which allowed the modeling of the dependent variable, FDI, as a function of its past and other variables dynamically over time. These results contribute to the evaluation of the assumptions made about the relationship between the components of Brazil Cost and the volume of direct investment in the country.
Due to an increasing economic instability worldwide, financial institutions are demanding more robust and powerful methodologies of credit risk modeling in order to ensure their financial health. The statistical model CreditRisk+, developed by Credit Suisse Financial Products (CSFP), is widely spread in the insurance market since it is not necessary to make assumptions. This is because the model is based on the default risk, that is, non-payment risk. The main goal of the above-mentioned model is to measure expected and non-expected losses in a credit portfolio. In order to measure default events, the model suggests grouping the debtors in exposure ranges so that the loss distribution can be approached to a Poisson. In the basic model, the default rates are fixed. To portray reality, we propose a new modeling in which the uncertainties and volatilities of default rates are incorporated. In this case, a new model which assumes a Gamma distribution in association with these uncertainties is defined. From the obtained distribution, not only is it possible to calculate the credit VaR (Value-at-Risk) but also the loss distribution and some point estimates, such as the expected loss in a certain period of time and the economic capital allocation. The main goal of this article is the CreditRisk+ model application with uncertainties in a segment of Brazilian industry. The economic capital allocation, that is, the difference between VaR and the expected deprival value is always higher, depending on the proposed modeling (with the incorporation of uncertainties, volatilities and the default rates). Our result is important, since financial institutions can be underestimating their losses in stressful moments.
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