How atmospheric pollutants impact the development of chronic obstructive pulmonary disease and lung cancer: A var-based model.

The impact of air pollution on humans is a worrisome factor that has gained prominence over the years due to the importance of the topic to society. Lung cancer and chronic obstructive pulmonary disease are among the diseases associated with pollution that increase the mortality rate in Brazil and worldwide. Therefore, this study aimed to determine the impacts of air pollutants on mortality rates from chronic obstructive pulmonary disease (COPD) and lung cancer (LC) using vector autoregressive (VAR) modeling. The adjusted model was a VAR(1) and, according to the Granger causality test, the air pollutants selected were PM10, O3, CO, NO2, and SO2. The shocks applied to the variables O3, using the impulse response function, negatively impacted COPD; in the eighth period, which is stabilized. The LC variable suffered more significant variations from O3 and after a shock in this variable, an initially negative response in LC occurred and the series stabilized in period nine. After one year, 20.19% of COPD variance was explained by O3. After twelve months, the atmospheric pollutant O3 represented 5.00% and NO2 represented 4.02% of LC variance. Moreover, the variables that caused the highest impact on COPD and LC mortality rates were O3 and NO2, indicating that air pollution influences the clinical state of people who have these diseases and even contributes to their development. The VAR model was able to identify the air pollutants that have the most significant impact on the diseases analyzed and explained the interrelationship between them.

Persistence effect determination of variability in forecasting of agricultural and road machinery national production / Determinação do efeito de persistência da variabilidade na previsão da produção nacional de máquinas agrícolas e rodoviárias

The objective of this research was to forecast the Brazilian national production of agricultural and road machinery in the short term by BOX & JENKINS methodology and determine the persistence effect. Data were obtained at National Association of Automotive Vehicle Manufacturers (ANFAVEA) from January 1960 to October 2019, totaling 718 monthly observations. The Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Conditional Heteroscedasticity (ARCH) methodology were used. The ARIMA (2,1,1)-ARCH (2) model was fitted and persistence of 0.60 was determined, showing that the instability in the series will be for a long period of time.(AU)

O objetivo desta pesquisa é prever a produção nacional de máquinas agrícolas e rodoviárias no Brasil, no curto prazo por meio da metodologia BOX & JENKINS e determinar o efeito de persistência na série. Os dados foram obtidos no site da Associação Nacional dos Fabricantes de Veículos Automotores (ANFAVEA) no período de janeiro de 1960 a outubro de 2019, totalizando 718 observações mensais. Os modelos Autoregressivos Integrados e de Médias Móveis (ARIMA) e de Heteroscedasticidade Condicional Autoregressiva (ARCH) foram utilizados para ajustar a média e a variabilidade da série. O modelo ARIMA(2,1,1) - ARCH(2) foi selecionado por meio das estatísticas de ajustes e a persistência determinada foi de 0,60 mostrando que a instabilidade na série é duradoura.(AU)

Persistence effect determination of variability in forecasting of agricultural and road machinery national production / Determinação do efeito de persistência da variabilidade na previsão da produção nacional de máquinas agrícolas e rodoviárias

ABSTRACT: The objective of this research was to forecast the Brazilian national production of agricultural and road machinery in the short term by BOX & JENKINS methodology and determine the persistence effect. Data were obtained at National Association of Automotive Vehicle Manufacturers (ANFAVEA) from January 1960 to October 2019, totaling 718 monthly observations. The Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Conditional Heteroscedasticity (ARCH) methodology were used. The ARIMA (2,1,1)-ARCH (2) model was fitted and persistence of 0.60 was determined, showing that the instability in the series will be for a long period of time.

RESUMO: O objetivo desta pesquisa é prever a produção nacional de máquinas agrícolas e rodoviárias no Brasil, no curto prazo por meio da metodologia BOX & JENKINS e determinar o efeito de persistência na série. Os dados foram obtidos no site da Associação Nacional dos Fabricantes de Veículos Automotores (ANFAVEA) no período de janeiro de 1960 a outubro de 2019, totalizando 718 observações mensais. Os modelos Autoregressivos Integrados e de Médias Móveis (ARIMA) e de Heteroscedasticidade Condicional Autoregressiva (ARCH) foram utilizados para ajustar a média e a variabilidade da série. O modelo ARIMA(2,1,1) - ARCH(2) foi selecionado por meio das estatísticas de ajustes e a persistência determinada foi de 0,60 mostrando que a instabilidade na série é duradoura.