Spatial Autocorrelation Test on the Average Unemployment in Indonesia

According to World Bank statistics in 2019, Indonesia ranked two in the average unemployment rate with 5.28% in South East Asia. Although the unemployment rate can be reduced by an equitable distribution of human resource empowerment and national development, the global pandemic COVID-19 made a major impact on increasing the rate of unemployment. This paper tests the spatial autocorrelation on the average unemployment in Indonesia using Ordinary Least Squares (OLS) and Moran's I. The OLS method was used to examine the effects that affect the unemployment rate using an independent variable. In contrast, the Moran's I used to prove the existence of spatial effect on the level of movement in Indonesia. From the experiment, there are four variables that influence the unemployment rate by using the OLS modeling method. The Moran's I test showed a p-value = 0.006 with α = 0.05. Therefore, there is a spatial autocorrelation between provinces in Indonesia. In addition, the model is tested using the Variance Inflation Factor. The model showed a VIF score ¡10, therefore there is no collinearity and the assumption is fulfilled. The model is also being tested using dwtest, bptest, and Lilliefors test. The result showed p-value = 0.6231 for dwtest, p-value = 0.932 for bptest, and p-value = 0.08438 for Lilliefors test.. © 2022 IEEE.

Understanding the shared working spaces: a Geography of Co-working supply in Delhi.

The present paper investigates the location pattern of co-working spaces in Delhi which is absent in the existing body of knowledge. Delhi is a political, administrative, educational, scientific and innovation capital that accommodates many co-working spaces in India. We developed Ordinary least squares (OLS) and geographically weighted regression (GWR) models to understand the associations of co-working spaces of digital labourers with other urban socio-economic, services and lifestyle variables in Delhi using secondary data for 117 coworking locations in 280 municipal wards of NCT-Delhi. Model diagnostic suggested that the GWR model provides additional information regarding geographical distribution of coworking spaces, and density of bars, median house rent, fitness centres, metro train stations, restaurants, cinemas, cafés, and creative enterprises are statistically significant parameters to estimate them. The importance of coworking spaces has increased in the post-disaster period, so this study informs public policies to benefit people and companies who choose coworking routes, and recommends urban planners, developers, and real-estate professionals to consider the proximity of creative industries in planning and developing coworking spaces in the future. Also, in the post COVID-19 period, to increase local jobs and long-term place sustainability, a localised policy intervention for coworking spaces in Delhi is highly recommended.

EU: The Effect of Energy Factors on Economic Growth

In this article, we investigate the effect of different energy variables on economic growth of several oil-importing EU member states. Three periods from 2000 to 2020 were investigated. Three different types of regression models were constructed via the gretl software. Namely, the OLS, FE, and SE approaches to panel data analysis were investigated. The FE approach was chosen as the final one. The results suggest the importance of the consumption of both oil and renewable energy on economic growth. Crises of certain periods also had a noteworthy effect as well.

Indirect inference estimation of higher-order spatial autoregressive models

This paper proposes estimating parameters in higher-order spatial autoregressive models, where the error term also follows a spatial autoregression and its innovations are heteroskedastic, by matching the simple ordinary least squares estimator with its analytical approximate expectation, following the principle of indirect inference. The resulting estimator is shown to be consistent, asymptotically normal, simulation-free, and robust to unknown heteroskedasticity. Monte Carlo simulations demonstrate its good finite-sample properties in comparison with existing estimators. An empirical study of Airbnb rental prices in the city of Asheville illustrates that the structure of spatial correlation and effects of various factors at the early stage of the COVID-19 pandemic are quite different from those during the second summer. Notably, during the pandemic, safety is valued more and on-line reviews are valued much less. © 2023 Taylor & Francis Group, LLC.

Augmenting commercial banks' other income through off-balance sheet activities in relation to their determinants in the Indian banking system

COVID-19 has a devastating impact on the global economy, particularly on robustness and resilience of emerging and developing economies' (EMDE's) economic-cum-financial systems. Reinventing banking practices with strategies are indispensable for sustainable growth. EMDEs like India have distinct country-specific business models. We aim to devise a sustainable model for augmenting banks' other income;analyzing off-balance sheet (OBS) activities in India, which may be applied in EMDEs' efficacy. We apply least-squares dummy variables and ordinary least squares models for fixed-effect regression analysis on OBS from 1996-2019. Regulatory determinants like capital adequacy, net non-performing assets, liquidity have more significant impact on OBS than bank-specific variables like bank size or macroeconomic like GDP. OBS can generate revenue is exemplified by strong relation to other income. Findings reveal that while assessing impact of COVID-19 on-balance sheets, banks should prioritize capital and contingency liquidity planning, focusing on OBS activities to augment other income in the revival strategy. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Sports Participation and Anti-Epidemic: Empirical Evidence on the Influence of Regular Physical Activity on the COVID-19 Pandemic in Mainland China.

This study aims to investigate the effects and influencing mechanisms of regular physical activity (RPA) on the COVID-19 pandemic. Daily data from 279 prefecture-level cities in mainland China were collected from 1 January to 17 March 2020. A two-way fixed-effects model was used to identify the causal relationship between physical activity and COVID-19, while also considering factors such as patterns of human behavior and socioeconomic conditions. The instrumental variable (IV) approach was applied to address potential endogeneity issues for a more accurate causal identification, and the mediating effect model was applied to examine the mechanisms of the influence of physical activity on the epidemic. We found that regular physical activity significantly improves individual immunity, which, in turn, leads to a reduction in the probability of being infected with COVID-19. Furthermore, we investigated the heterogeneity of the influence, finding that the negative impact of physical activity on the pandemic is more pronounced in the absence of adequate medical resources, strong awareness of prevention among residents, and fully implemented public health measures. Our results provide empirical evidence for the mechanisms of influence of physical activity on the pandemic. We would suggest that not only should physical activity be actively practiced during the pandemic, but also long-term regular exercise habits should be consciously cultivated to improve the ability of the individual immune system to better cope with sudden outbreaks of emerging infectious diseases.

Using gravity model to make store closing decisions: A data driven approach.

Many studies propose methods for finding the best location for new stores and facilities, but few studies address the store closing problem. As a result of the recent COVID-19 pandemic, many companies have been facing financial issues. In this situation, one of the most common solutions to prevent loss is to downsize by closing one or more chain stores. Such decisions are usually made based on single-store performance; therefore, the under-performing stores are subject to closures. This study first proposes a multiplicative variation of the well-known Huff gravity model and introduces a new attractiveness factor to the model. Then a forward-backward approach is used to train the model and predict customer response and revenue loss after the hypothetical closure of a particular store from a chain. In this research the department stores in New York City are studied using large-scale spatial, mobility, and spending datasets. The case study results suggest that the stores recommended being closed under the proposed model may not always match the single store performance, and emphasizes the fact that the performance of a chain is a result of interaction among the stores rather than a simple sum of their performance considered as isolated and independent units. The proposed approach provides managers and decision-makers with new insights into store closing decisions and will likely reduce revenue loss due to store closures.

A Nonlinear Transformation Methods Using Covid-19 Data in the Kurdistan Region

Ordinary Least squares (OLS) are the most widely used due to tradition and their optimal properties to estimate the parameters of linear and nonlinear regression models. Nevertheless, in the presence of outliers in the data, estimates of OLS become inefficient, and even a single unusual point can have a significant impact on the estimation of parameters. In the presence of outliers is the use of robust estimators rather than the method of OLS. They are finding a suitable nonlinear transformation to reduce anomalies, including non-Additivity, heteroscedasticity, and non-normality in multiple nonlinear regression. It might be beneficial to transform the response variable or predictor variable, or both together to present the equation in a simple, functional form that is linear in the transformed variables. To illustrate the superior transformation function, we compare the squared correlation coefficient (coefficient of determination), Breusch-Pagan test, and Shapiro-Wilk test between the transformation functions. © 2022 IEEE.

Electricity Load Forecasting under COVID-19

The outbreak of novel coronavirus disease in 2020 has profoundly impacted all aspects of lives and posed a unique challenge in energy load forecasting. With the increase of the COVID-19 cases, governments worldwide impose strict social distancing and limit the mobility of the population, which causes a shift in load consumption magnitude and pattern. In this paper, we first identify the most influential COVID-19 features for load reduction. Then, we propose a new load forecasting model that includes the new features. The case study on the New York City data set demonstrates that our new forecasting model can efficiently provide new load prediction in the pandemic period. © 2021 IEEE.

Effects of Social Mobility and Stringency Measures on the COVID-19 Outcomes: Evidence From the United States.

This paper examines the effects of stringency measures (provided by the Oxford Coronavirus Government Response Tracker) and total time spent away from home (provided by the Google COVID-19 Community Mobility Reports) on the COVID-19 outcomes (measured by total COVID-19 cases and total deaths related to the COVID-19) in the United States. The paper focuses on the daily data from March 11, 2020 to August 13, 2021. The ordinary least squares and the machine learning estimators show that stringency measures are negatively related to the COVID-19 outcomes. A higher time spent away from home is positively associated with the COVID-19 outcomes. The paper also discusses the potential economic implications for the United States.

Analyses the effects of COVID-19 outbreak on human sexual behaviour using ordinary least-squares based multivariate logistic regression.

This study aimed to evaluate the impact of COVID-19 on sexual, mental and physical health. There were 262 respondents included in this study (38% female and 62% male) above 18 years of age from India. Statistical analysis was performed using Ordinary Least Squares (OLS) based on multivariate logistic regression analysis. The numerical tests were performed by using Python 3 engine and R-squared (coefficient of multiple determinations for multiple regressions) for prediction and P value > 0.5 is considered to be statistically significant. The study outcomes were obtained using a study-specific questionnaire to assess the quality of sex life, changes in sexual behavior and mental health. Frequency of sexual intercourse, frequency of watching porn, sexual hygiene, frequency of physical activity, depression, desire for parenthood in female respondents have more significant R 2 (0.903, 0.976, 0.973, 0.989, 0.985, 0.862) value respectively as compared to male respondents. Financial anxiety, Smoking and drinking habits in male respondents have more significant R 2 (0.917, 0.964) value respectively as compared to female respondents. The aim of this study is to understand quality of sex life, sexual behavior, reproductive planning, mental health, physical health and adult coping during the COVID-19 pandemic, as well as how past experiences have affected. Many respondents had a broad variety of problems concerning their sexual and reproductive well being. Measures should be set in order to safeguard the mental and sexual health of people during the pandemic.

Predictive modelling of COVID-19 confirmed cases in Nigeria.

The coronavirus outbreak is the most notable world crisis since the Second World War. The pandemic that originated from Wuhan, China in late 2019 has affected all the nations of the world and triggered a global economic crisis whose impact will be felt for years to come. This necessitates the need to monitor and predict COVID-19 prevalence for adequate control. The linear regression models are prominent tools in predicting the impact of certain factors on COVID-19 outbreak and taking the necessary measures to respond to this crisis. The data was extracted from the NCDC website and spanned from March 31, 2020 to May 29, 2020. In this study, we adopted the ordinary least squares estimator to measure the impact of travelling history and contacts on the spread of COVID-19 in Nigeria and made a prediction. The model was conducted before and after travel restriction was enforced by the Federal government of Nigeria. The fitted model fitted well to the dataset and was free of any violation based on the diagnostic checks conducted. The results show that the government made a right decision in enforcing travelling restriction because we observed that travelling history and contacts made increases the chances of people being infected with COVID-19 by 85% and 88% respectively. This prediction of COVID-19 shows that the government should ensure that most travelling agency should have better precautions and preparations in place before re-opening.