U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression

Artificial neural network (ANN) has been showing its superior capability of modeling and prediction. Neural network model is capable of incorporating high dimensional data, and the model is significantly complex statistically. Sometimes, the complexity is treated as a Blackbox. However, due to the model complexity, the model is capable of capture and modeling an extensive number of patterns, and the prediction power is much stronger than traditional statistical models. Random forest algorithm is a combination of classification and regression trees, using bootstrap to randomly train the model from a set of data (called training set) and test the prediction by a testing set. Random forest has high prediction speed, moderate variance, and does not require any rescaling or transformation of the dataset. This study validates the relationship between the U.S. unemployment rate and economic indices during the COVID-19 pandemic and constructs three different predictive modeling for unemployment rate by economic indices through neural network, random forest, and generalized linear regression model.

Aggregative Methodology of Aircraft Cooperative Production Project Efficiency Evaluation

The overview of modern forms and trends of international cooperation in aviation industry is presented. The actuality of choice of cooperation schemes and main indices values of cooperation production for the aviation industry of Ukraine is determined. The analysis of dependences of key technical and economic indices of the Project of creation of aircraft cooperative production is carried out and the aggregative methodology of estimation of aircraft cooperative production project efficiency is offered.

The impact of socio-economic indices on the incidence of the child population of the Republic of Bashkortostan

Introduction. Over the past two decades, a large amount of data has been accumulated that show the significant impact of social factors on the health of the population. The Republic of Bashkortostan is a large industrial centre and one of the most promising subjects of the Russian Federation. Purpose of the study. Ranking the territories of the Republic of Bashkortostan by priority socioeconomic indices, as well as determining their impact on the health of the child population. Material and methods. As the initial data, the materials of the socioeconomic state of the Republic of Bashkortostan, data on the number and morbidity of the child population for the period 2014-2018 were used. Correlation-regression analysis was carried out, and qualitative assessments of the results obtained were given. The principle of dividing the territory into seven socio-economic zones, taking into account climatic and geographical features, the development of industrial potential and the existing socio-economic ties, was chosen as the basis for the study. Results. The ranking of territories by socio-economic indices in the Republic of Bashkortostan showed that most of the municipalities (over 60%) have a low level of socio-economic development. The most favourable conditions in terms of social comfort for children were found in the southern, central and northwestern economic zones. So, as socio-economic indices improve by 2018, the incidence of the population tends to decrease. Conclusion. Thanks to the data obtained, a number of the most disadvantaged areas in socio-economic development and morbidity in the child population have been identified. In these territories, it is recommended to develop a set of measures to improve and stabilise socio-economic indices.

Comparative Analysis of Spatial Disparities Based on Infrastructure Development and Socioeconomic Indices: A Case Study of Lahore

Infrastructure development and socioeconomic factors are directly related to the opulence and economic growth of any region. Disparity in the allocation of resources within a city has a huge influence on the socioeconomic factors of the population. Cities in developing countries manifesta vast disparity in the provision of infrastructural facilities and it leads to curbing the socioeconomic development of their residents. The current research aims to study the impact of disparity in infrastructure development on the socioeconomic factors has been assessed in the city of Lahore. Two towns of the city, that is, Johar town and Shalamar town were selected based on two different criteria to examine the impact of disparity in infrastructure development on the prevailing socioeconomic conditions. Primary data was collected with the help of a questionnaire encompassing major infrastructure development factors and socioeconomic indicators. Surveys were conducted in these towns with the disproportionate technique of stratified sampling. Data was analyzed through SPSS. Statistical Linear Regression Model was applied to determine whether a relationship exists among the infrastructural and socioeconomic indicators or not. The results showed that the town with better infrastructure development has far better socioeconomic conditions as compared to the less developed town within the same city. Keywords: inequality, infrastructural development, socio-economic indices, spatial disparities

A New Empirical Approach for Estimating Solar Insolation Using Air Temperature in Tropical and Mountainous Environments

Solar irradiance is an available resource that could support electrification in regions that are low on socio-economic indices. Therefore, it is increasingly important to understand the behavior of solar irradiance. and data on solar irradiance. Some locations, especially those with a low socio-economic population, do not have measured solar irradiance data, and if such information exists, it is not complete. There are different approaches for estimating solar irradiance, from learning models to empirical models. The latter has the advantage of low computational costs, allowing its wide use. Researchers estimate solar energy resources using information from other meteorological variables, such as temperature. However, there is no broad analysis of these techniques in tropical and mountainous environments. Therefore, in order to address this gap, our research analyzes the performance of three well-known empirical temperature-based models—Hargreaves and Samani, Bristol and Campbell, and Okundamiya and Nzeako—and proposes a new one for tropical and mountainous environments. The new empirical technique models daily solar irradiance in some areas better than the other three models. Statistical error comparison allows us to select the best model for each location and determines the data imputation model. Hargreaves and Samani’s model had better results in the Pacific zone with an average RMSE of 936,195 Wh/m2 day, SD of 36,01%, MAE of 748,435 Wh/m2 day, and U95 of 1.836,325 Wh/m2 day. The new proposed model showed better results in the Andean and Amazon zones with an average RMSE of 1.032,99 Wh/m2 day, SD of 34,455 Wh/m2 day, MAE of 825,46 Wh/m2 day, and U95 of 2.025,84 Wh/m2 day. Another result was the linear relationship between the new empirical model constants and the altitude of 2500 MASL (mean above sea level).

Assessment of Heatwave Vulnerability in Korea Considering Socio-economic Indices

The rising heatwave occurrences in recent times due to climate change have resulted in increased mortalities and socio-economic damage. Consequently, several studies have been conducted to examine heatwave vulnerability in Korea. However, most of these studies used the IPCC vulnerability framework and weighting techniques, such as the equal weight and AHP methods, which lacked objectivity in the process of calculating vulnerability. This study employed socio-economic data to measure the heatwave vulnerability index for individual local governments in Korea using the principal component analysis and entropy weighting methods. These techniques ensure that the aggregation of proxies and the weighting process remain objective, unlike previous studies. According to the obtained results, rural areas such as Jeollanam-do, Jeollabuk-do, Gyeongsangnam-do, and Gyeongsangbuk-do, and relatively decrepit urban areas demonstrated high vulnerability scores. In addition, a positive correlation was found between the calculated vulnerability index and mortalities from the recent heatwaves. The heatwave vulnerability index developed in this study can therefore be used to form effective heatwave response policies suited to the conditions of each local government.

Happiness as an Interdisciplinary Construct: Options for Sociological Conceptualization and Operationalization

The project seeks to clarify the conceptual and operational definitions of happiness to be referred to in empirical sociological studies, and to systematize methodological approaches to the sociological analysis of happiness, outlining the cognitive potential and limitations of these approaches. Conceptually, the project indicates that the achievements to date can be grouped into two conventional areas of further studies: the objectivistic econometric approach (happiness indices based on social and economic indices) and the subjectivistic social and psychological approach (self-assessments in terms of happiness derived from public surveys). In terms of methods, the authors of the project organized the indicators and methods used in the empirical study of happiness, allowing the use of diverse survey tools in the observational studies aimed at identifying commonplace perception of happiness and adequately “measuring” its level.

Synthesis of stable coalitions in the economy based on fundamental criteria of the portfolio theory

Subject. The article outlines basic principles of a mathematical model for formation of stable coalitions in the economy at the interstate level. Objectives. We focus on developing a mathematical model to build such coalitions. Methods. The study employs the portfolio theory, risk-return model, correlation and regression analysis, technical and fundamental analysis. The proposed model rests on fundamental provisions of creating a multicomponent, widely diversified investment portfolio. The model uses the key concepts, like expected profitability, risk level, industry diversification and hedging, in combination with the synthesis of a diversified group of leading commodity indices. Results. We show possibilities of using internal (based on the country’s indices) and external (based on other countries’ indices) correlation analysis, according to data on trends in economic indices, to ensure sectoral diversification within the country and maximize the international level of sectoral diversification, respectively. We performed a fundamental analysis of the condition of economies of the countries included in the coalition, and of the countries, which are considered to be included in the coalition as appropriate. The paper assesses positive and negative factors of joint functioning of the economies of the coalition countries, from the point of view of their geographic location. Conclusions. The model makes it possible to build new optimal coalitions in the economy, to analyze the practicability of further existence of previously formed coalitions, and to update the composition of coalitions, according to trends in the world economy development.

An Analysis of Inter-Relationship among Commodities, Stock and Economic Indices

This research will concentrate primarily on commodity relationships, mainly, prices of oil (OP) and gold (GP), US stock market (S&P500), consumer confidence index (CCI), the US Dollar index (USDX), and the industrial production (IP). The purpose of the analysis is to study the dynamic interconnection between OP, USDX, CCI, GP, IP, and S&P500, by estimating the Vector Auto Regression (VAR) model. OP, CCI, GP, USDX, S&P500, and IP are the different variables used in this paper. Using monthly data from January 1971 to May 2020, this study applies the Granger causality test, Variance Decomposition (VDC) analysis, and Impulse Response Function (IRF). It can be inferred from the results that USDX has a significant relationship with GP and has a causal impact on GP. Industrial Production has also shown a significant relationship with S&P500 and has a causal impact on S&P500. The result also suggested that CCI and S&P500 share a unidirectional relationship; the volatility in CCI in the short run is due to the S&P500. Also, the variables do not have any other significant relationship. The findings also highlighted that USDX directly affected GP negatively. Industrial production directly impacted S&P500 in the short run, while a positive relationship is shared between CCI and S&P 500.

Biological and economic efficacy of cowpea-radish association fertilized with organic manure in diverse planting arrangements

Green manuring and the spatial arrangement of planting intercropped crops are manageable factors to increase the bioeconomic effectiveness of intercropped systems. Therefore, the object of this study was to work out the bio-economic efficacy in cowpea-radish association under diverse Calotropis procera biomass amounts and planting arrangements in two cultivation seasons through biological and economic indices. The research was conducted in a design of randomized complete blocks with four repetitions. The treatments were made of combination of four C. procera biomass amounts placed into the soil (20, 35, 50 and 65 t ha-1) with three cowpea-radish planting arrangements (2:2, 3:3 and 4:4). The biological indices, land equivalent ratio (LER), area-time equivalency ratio (ATER), actual yield loss (AYL), and system productivity index (SPI) and the economic indicators gross revenue (GR), net revenue (NR), rate of return (RR) and profit margin (PM) were evaluated to express the bio-economic efficacy of the cowpea-radish association. The greatest biological efficiencies of the cowpea-radish association were attained with LER and ATER of 1.75 and 1.25; AYL and SPI of 1.48 and 13.15 t ha-1, respectively, in the amount of 62 t ha-1 of C. procera biomass in the planting arrangements 2: 2 and 3: 3. The largest net economic revenue (NR) of 16,382.85 R$ ha-1 was attained in the amount of 52 t ha-1 of C. procera in the planting arrangement 3: 3.