RESUMEN
ABSTRACT: This study aimed to price croplands in Rio Grande do Sul State (southern Brazil) and point which variables had the most significant impact on prices. The main purpose was achieved using multiple linear regression and principal component analysis. The variables used in this study were planted area, production, price, and yield of the commodities soybean, wheat, and corn. The period under analysis was from January 1994 to December 2017 (biannual observations). Multiple linear regression showed that five variables contributed to land pricing, being three related to soybean and two to wheat. Multivariate analysis grouped the investigated variables into clusters and indicated their influence, in addition to providing information on land prices and reducing variable dimensionality from fourteen original variables to three principal components to be analyzed. The two analyses complemented each other so that the croplands' price was explained by three variables, in which two corroborated in constructing the pricing model for croplands.
RESUMO: Este estudo teve como objetivo a precificação de terra para lavouras no Rio Grande do Sul e apresentar quais variáveis possuem maior impacto no preço. O objetivo foi alcançado por meio da aplicação da análise de regressão linear múltipla e de componentes principais. Variáveis relacionadas às commodities soja, trigo e milho, como a área plantada, produção, cotação e rendimento, formaram o banco amostral para as duas metodologias, compreendendo o período de janeiro de 1994 a dezembro de 2017, em observações bianuais. A regressão linear múltipla mostrou que três variáveis relacionadas à soja e duas ao trigo contribuem na precificação das terras. A análise multivariada agrupou as variáveis investigadas, indicando a influência entre as mesmas, fornecendo informações sobre o preço de terras e diminuindo a dimensionalidade do problema de 14 variáveis originais para três componentes a serem analisados. As duas análises se complementaram de forma que o preço de terras foi explicado por três variáveis e duas corroboraram na construção do modelo de precificação das lavouras.
RESUMEN
This study aimed to price croplands in Rio Grande do Sul State (southern Brazil) and point which variables had the most significant impact on prices. The main purpose was achieved using multiple linear regression and principal component analysis. The variables used in this study were planted area, production, price, and yield of the commodities soybean, wheat, and corn. The period under analysis was from January 1994 to December 2017 (biannual observations). Multiple linear regression showed that five variables contributed to land pricing, being three related to soybean and two to wheat. Multivariate analysis grouped the investigated variables into clusters and indicated their influence, in addition to providing information on land prices and reducing variable dimensionality from fourteen original variables to three principal components to be analyzed. The two analyses complemented each other so that the croplands' price was explained by three variables, in which two corroborated in constructing the pricing model for croplands.
Este estudo teve como objetivo a precificação de terra para lavouras no Rio Grande do Sul e apresentar quais variáveis possuem maior impacto no preço. O objetivo foi alcançado por meio da aplicação da análise de regressão linear múltipla e de componentes principais. Variáveis relacionadas às commodities soja, trigo e milho, como a área plantada, produção, cotação e rendimento, formaram o banco amostral para as duas metodologias, compreendendo o período de janeiro de 1994 a dezembro de 2017, em observações bianuais. A regressão linear múltipla mostrou que três variáveis relacionadas à soja e duas ao trigo contribuem na precificação das terras. A análise multivariada agrupou as variáveis investigadas, indicando a influência entre as mesmas, fornecendo informações sobre o preço de terras e diminuindo a dimensionalidade do problema de 14 variáveis originais para três componentes a serem analisados. As duas análises se complementaram de forma que o preço de terras foi explicado por três variáveis e duas corroboraram na construção do modelo de precificação das lavouras.
Asunto(s)
Modelos Lineales , Análisis de Regresión , Costos y Análisis de CostoRESUMEN
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.