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• More than two-thirds of the world's population are affected by the escalating issue of rising income inequality. This phenomenon is intensifying the risk of societal divisions, provoking protests in numerous countries, and impeding economic and social development. • BioTrade refers to products or services derived from biodiversity, which are commercialized and traded in a manner that upholds respect for both people and nature. • There exists a robust positive correlation between BioTrade and income inequality. However, the impact of BioTrade on inequality exhibits heterogeneity based on sub-regions and the distribution of income disparities. • The level of preparedness for advanced technologies serves as one pathway through which BioTrade influences global inequality. While empirical studies exploring the impacts of international trade on income inequality have continued to expand, yielding mixed results, less attention has been devoted to the effects of BioTrade on income redistribution. This article addresses this gap by examining the impact of BioTrade on income inequality, with a specific emphasis on the role of technological innovations. We employ a panel model encompassing 131 countries over the period 2010–2019, utilizing instrumental variable two-stage least squares and smoothed instrumental-variable quantile regression. Overall, the results indicate a strong positive link between BioTrade and income inequality, yet its effects remain heterogeneous across sub-regions and the distribution of income inequality. To elucidate this positive link, the article presents evidence of the crucial role of the level of readiness for advanced technologies. Consequently, technological innovations play a pivotal role in mitigating the accelerating effects of BioTrade on inequality. The results are robust to a battery of robustness tests, including variations in estimation methodologies, alternative measures of inequality and BioTrade, and the inclusion of control variables. These results call upon policymakers to champion technological innovations, such as Internet access and research and development, as crucial strategies for effectively and sustainably alleviating the aggravation of inequalities induced by BioTrade. [ABSTRACT FROM AUTHOR]

Despite the cross-border availability of almost all goods and services online due to global Internet access, the domestic origin of sellers remains significant. This study examines the preferences for domestic versus cross-border goods and services in online purchases in the EU online market from 2020 to 2023. We use quantitative methods including ordinary least squares (OLS), decision trees, and spatial autocorrelation analysis. We find significant effects of currency, language(s), and Internet use on domestic online purchases, while cross-border online purchases are further influenced by prices and urbanization. Our analysis reveals patterns based on the origin of the seller: domestic, intra-EU, or non-EU seller. There is a strong preference for electronic goods and services, regardless of the seller's origin, while physical goods show a decreasing preference from domestic to intra-EU and non-EU sellers. Limited geographical effects and spatial patterns in online retailing were found, with a trend towards domestic localization. These differences in e-commerce by seller origin are primarily driven by country-specific characteristics (language(s), currencies) rather than geographic distance. The variation in the purchase of goods and services also depends on their physical and electronic form, that is, digital ordering and/or digital delivery. The expansion of e-commerce and the importance of country-specific characteristics require the development of standards to measure these influences. [ABSTRACT FROM AUTHOR]

Insomnia is a prevalent sleep disorder that causes serious harm to individuals and society. There is growing evidence that environmental factors may be associated with sleep disorders, but few studies have explored the relationship between insomnia and urban functional structure from a spatial perspective. This study collected multi-source big data (e.g., insomnia posts on Weibo, locations of urban facilities on Baidu) and explored the effects of different urban spatial element configurations on residents' insomnia. The ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to quantify the global and local effects of urban functional categories on residents' insomnia. The results showed that the quantity of catering service facilities accounted for the largest proportion, and the consumer function was the most consistent with the distribution of insomnia. There is a domain relationship between the incidence of insomnia and urban functional zones. It has the strongest correlation with employment mixed functional zones and the weakest with residential mixed functional zones. These findings could serve as references for the functional structure and layout of urban space for improving the sleep health of residents and benefit for urban health. [ABSTRACT FROM AUTHOR]

The gender gap in education is not limited to the subject of mathematics or only to be found in high-income countries, and its repercussions can have generational consequences. Previous literature has sought to explain why the gender gap persists in most high- and middle-income countries vis-à-vis social gender inequality without drawing from similarly comprehensive data from the African context with its unique gender gap landscape. This paper examines the correlation between more gender-equal countries and the gender gap in student test scores for reading, mathematics, and HIV-AIDS awareness in southern and eastern Africa. We use ordinary least squares and quantile regression to analyse student information from the SACMEQ III dataset in comparison with measurements of social gender inequality such as the Gender Inequality Index and the Global Gender Gap Index. As such, this analysis draws on data from roughly 60,000 African students and includes test scores from reading comprehension and HIV-AIDS awareness, a unique subject that is critical for health, wealth and wellbeing on the continent. Our study finds that in each subject at every quantile, the gender gap displays a robust inverse relationship with more gender-equal countries specifically demonstrating higher educational performance among girls. With youthful populations and burgeoning economies, these countries in Africa have much to gain from addressing the gender gap in education. These findings encourage policymakers to consider how the social condition of gender-equality might influence the academic performance of students, especially for girls. [ABSTRACT FROM AUTHOR]

The NEET rate in Indonesia is the highest among other ASEAN countries and has increased in 2022, coinciding with the first year JKP benefits can be claimed. The purpose of this study is to see and analyze the effect of JKP on the percentage of NEET in Indonesia. Using August 2022 sakernas data and 2021 podes data in the form of cross-section data, this study was conducted using the Ordinary Least Square method. The analysis used in this research is descriptive analysis and inferential analysis in the form of regression analysis. The results of the analysis show that a 1% increase in the number of youth JKP recipients in the district/city reduces the percentage of NEET youth by 0.62% in 2022. Suggestions for the government are to increase JKP participation among youth aged 15-24 years. Review the requirements, claim procedures and obligations of JKP beneficiaries, to mitigate the risk of moral hazard. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to analyse the weekday effect on the Nifty Shariah indices as per the Islamic calendar. The study is intended to know about the return and volatility of these indices during Ramadhan and non-Ramadhan days. Design/methodology/approach: The study focuses on analysing the Nifty Shariah indices and Sensex daily returns collected from NSE India and BSE India, respectively, during the period of 1 August 2016 to 31 July 2022. Descriptive statistics are used to analyse the data, while the Ordinary Least Square method is used to determine the impact of weekdays on the Nifty Shariah indices. Additionally, the study applies the GARCH statistical model to examine the influence of Ramadhan on the returns and volatility of the Nifty Shariah indices. Findings: All of the Nifty Shariah indices produced positive returns during the overall sample period. According to the study, Tuesday index returns outperform other weekdays. The GARCH model indicated that the coefficient values for the Nifty 50 Shariah and Nifty 500 Shariah indices were negative. Ramadhan has a strong negative effect on volatility, according to this study. Originality/value: The outcomes of the research are beneficial for investors aiming to exploit daily or weekly price fluctuations, rather than pursuing extended investment periods. Furthermore, fund managers can employ these findings to shape trading strategies, and academics can examine the performance of Shariah indices in the Indian context. This enables devout investors to make significant financial choices, thus advancing ethical values in society and upholding standards of both public and private morality. [ABSTRACT FROM AUTHOR]

Despite the cross-border availability of almost all goods and services online due to global Internet access, the domestic origin of sellers remains significant. This study examines the preferences for domestic versus cross-border goods and services in online purchases in the EU online market from 2020 to 2023. We use quantitative methods including ordinary least squares (OLS), decision trees, and spatial autocorrelation analysis. We find significant effects of currency, language(s), and Internet use on domestic online purchases, while cross-border online purchases are further influenced by prices and urbanization. Our analysis reveals patterns based on the origin of the seller: domestic, intra-EU, or non-EU seller. There is a strong preference for electronic goods and services, regardless of the seller’s origin, while physical goods show a decreasing preference from domestic to intra-EU and non-EU sellers. Limited geographical effects and spatial patterns in online retailing were found, with a trend towards domestic localization. These differences in e-commerce by seller origin are primarily driven by country-specific characteristics (language(s), currencies) rather than geographic distance. The variation in the purchase of goods and services also depends on their physical and electronic form, that is, digital ordering and/or digital delivery. The expansion of e-commerce and the importance of country-specific characteristics require the development of standards to measure these influences.

Nowadays with the climate change the environmental degradation has became the crucial issue in the World. This study empirically investigates the impact of agriculture value-added, forest area, and renewable energy on CO2 emissions in GUAM union countries from 1996 to 2019. The independent variables in this study are agriculture value-added, forest, renewable energy and the dependent variable is CO2 emissions. The statistical methods as the Panel unit root test, Pedroni and Kao panel co-integration test and OLS, FMOLS, and DOLS long-run tests were employed for the empirical part of the paper. The independent variables in this study are agriculture value-added, forest, renewable energy and the dependent variable is CO2 emissions. The statistical methods as the Panel unit root test, Pedroni and Kao panel co-integration test and OLS, FMOLS, and DOLS long-run tests were employed for the empirical part of the paper. The outputs of the Pedroni and Kao panel co-integration tests confirmed that there is a long-term relationship between the analyzed series. The findings of the OLS, FMOLS, and DOLS tests indicate a negative relationship between the analyzed variables. According to the the results of empirical analyzes it was confirmed that there is a statistically significant and negative relationship between agriculture value-added, forest, renewable energy and CO2 emissions which means that an increase of agricultural production, forest areas and renewable energy consumption decreased the CO2 emissions in GUAM countries for the time span 1996-2019.

Objective: Literature on the influence of homelessness on crime is lacking, particularly at the neighborhood level. This study seeks to understand how homelessness affects crime levels in a large city in the United States. Data/Methods: Crime data, homeless count data, and neighborhood characteristics are obtained from three government public data sources. OLS Regression and Geographically Weighted Regression (GWR) are utilized to determine the relationship between homelessness and crime on a global and local level. Results: The analyses revealed that homelessness is significantly, positively associated with crime, even when controlling for macro-level correlates of crime. The effect size of homelessness on crime varies across neighborhoods. Conclusions/Implications: Policy implications based on findings of the current study and future research are discussed. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to investigate determinants of banks profitability in CBE by using time series data of CBE from year2003 to 2015. Design/methodology/approach: The research has econometric research design. The study used quantitative research approach and secondary financial data are analyzed by using Vetcor Auto Regressive Model (VAR), for the bank profitability was measureed by Return on Asset (ROA). OLS method was applied to investigate the impact of bank size, managerial efficiency, liquidity, credit risk, real GDP growth rate, and annual inflation rate. Findings: The empirical results shows that bank specific factors; bank size, managerial efficiency, credit risk and macro-economic factors; level of GDP and annual inflation rate have a strong influence on the profitability of banks. The empirical study shows that, all explanatory variables in this study have negative effect on the profitability measure except GDP and inflation rate. In addition to this, without liquidity all explanatory variables are statistically significant. Practical implications: Management bodies of CBE should strive to strengthen the bank specific factors like capital position, managerial efficiency, credit risk, asset quality, and bank size. Because, The competition become tough since increase in new entrant to the market, banks are increasingly being substituted by the general public as a source of funds by new share companies being established in a variety of sectors, and the micro-finance industry continues to show rapid growth. Originality/value: The banking industry is crucial source of financing different business segments that is operated in a given country. [ABSTRACT FROM AUTHOR]

Artificial impermeable surfaces are becoming more prevalent, especially in urban areas, as a result of shifting land use and cover, roads, roofs, etc. The modification of land surface temperature (LST) can also be accomplished through artificially impermeable surfaces. Large artificial impermeable surfaces, such as rooftops, parking lots, and other areas of use, can be found in industrial zones, shopping malls, industrial airports, and other locations. For the Anatolian side of Istanbul, 14 Landsat 8 OLI/TIRS imagery images over the years 2016–2022 were investigated. To evaluate how well the study's images could be utilized, correlation and cosine similarity approaches were employed. A total of 12 images may be employed for research LST studies, it was discovered. We looked at closure dates during the COVID-19 epidemic to find out how human migration affected the LST. In addition, the LST value was estimated using the ordinary least squares (OLS) method employing LST and other biophysical indices. A decrease in LST values was seen as a result of the investigation. High levels of similarity and correlation were found between the images used. Results from the Google Mobility Index also provide support to the study. All of these facts provide support to Istanbul's Anatolian side experiencing lower surface temperature values, which consequently affects the city's massive structures. [ABSTRACT FROM AUTHOR]

This paper assesses the effects of social media on economic growth in a sample of 177 countries. The originality of the article lies in highlighting the direct and indirect effects of social media externalities on the process of economic growth. Unlike existing works, we study this nexus from a global perspective using a cross-sectional model. To achieve this, we specify and estimate a panel data model using ordinary least squares (OLS) methods over the period 2012–2019. The robustness of the results has been proven by using Poisson pseudo maximum likehood (PPML) and the quantile regression (QR). The results show that social media as measured by Facebook penetration improves economic growth. Furthermore, the results of the mediation analysis show that the effect of social media on the economic growth is mediated by financial development, human capital, information and communication technologies (ICT), electricity consumption, and political stability. We suggest, in addition to the quantitative and qualitative strengthening of the telecommunication infrastructure, a rational use of social media for a better consolidation of economic growth. [ABSTRACT FROM AUTHOR]

Anomalies in stock markets have been studied with varied approaches worldwide, and mixed outcomes exist. The present study has investigated the financial year and calendar year effect for indices from Indian markets. Sectoral indices from Indian markets have been incorporated in this study on the basis of their weightage in the stock market. Data from 2011 to 2023 have been engaged with historical prices of these indices taken from the stock exchange websites. The analysis has been carried out using the ordinary least squares regression method and the independent sample t-test. Weak anomalies have been documented in the case of the financial services sector. Therefore, the findings indicate rare possibilities for investors to gauge abnormal returns with strategies related to anomalies. With increasing transparency in the online trading mechanism, such possibilities cease to exist for investors. Moreover, market dynamics have transformed on account of several shocks such as COVID-19, Omicron, Russia-Ukraine, Visa-related international issues in the world over economies and financial markets. Investors and markets have become more cautious and look out for a precautionary approach while dealing in stock market trading. [ABSTRACT FROM AUTHOR]

Bài báo xem xét tác động của đầu tư công tới Năng suất nhân tố tổng hợp -TFP của tỉnh Bình Định. Nghiên cứu sử dụng kết hợp nhiều phương pháp khác nhau như thống kê mô tả, so sánh, tổng hợp và mô hình kinh tế lượng với các cách ước lượng như OLS, REM, FEM, 3SLS, ARDL với số liệu thứ cấp từ niên giám thống kê của tỉnh Bình Định, từ bộ chỉ số năng lực cạnh tranh cấp tình - PCI và chỉ số sẵn sàng ứng dụng công nghệ thông tin trong giai đoạn 2005- 2021. Kết quả cho thấy, đầu tư công tác động cải thiện TFP chung của nền kinh tế và của khu vực kinh tế tư nhân và tác động này được duy trì trong dài hạn. Ngoài ra, một phát hiện rút ra là năng lực chuyển đổi số của tỉnh có tác động tích cực tới TFP của tỉnh dài hạn.

This paper aims to theoretically and empirically test whether the Weather Index Insurance for Mutton Sheep (WIMS) which protects the herders from increased feeding costs resulting from drought and snow disasters promotes the income of herders in China's Inner Mongolia or not. We have applied the OLS, PSM, and quantile regression models using field survey data from 261 herders in Xilin Gol, Inner Mongolia Autonomous Region for our objective. The findings demonstrate that the WIMS significantly increases the overall and high-income herders' income, but has no significant effect on herders' income at other income levels. According to the results, we recommend that as the subsidized agricultural insurance product, the government should expand the pilot region of WIMS to furtherly test its income effect and consider reducing or exempting insurance premiums for low-income herders to reduce their financial pressure. [ABSTRACT FROM AUTHOR]

This research investigates the relationship between exchange rates and economic growth in Nigeria during the period from 1981 to 2019. The research conducted an analysis of Nigerian data using the Ordinary Least Square (OLS) technique and identified that Exchange Rates have a favourable impact on Economic Growth. This paper affirmed the regression's validity with stationary residuals and found no long-term equilibrium using Bound Cointegration. The ARDL modelling revealed short-term connections between exchange rates and economic growth. Positive correlations were observed between exchange rates, GDP per capita growth, interest rates, and total exports, while negative relationships were noted with inflation and total imports, highlighting the importance of exchange rate stability in sustaining economic growth. This research provides a novel viewpoint on the connection between exchange rates and economic growth in Nigeria during the period from 1981 to 2019. It uncovers that more than 98% of the fluctuations in exchange rates can be accounted for by the included variables. The noteworthy discovery of the absence of a long-term equilibrium relationship within the specified time frame sets this study apart. Furthermore, it underscores the crucial role of exchange rate stability in promoting enduring economic growth, emphasising the necessity for well-designed policies that consider the intricate economic landscape. [ABSTRACT FROM AUTHOR]

Given an exogenous treatment d and covariates x, an ordinary least-squares (OLS) estimator is often applied with a noncontinuous outcome y to find the effect of d, despite the fact that the OLS linear model is invalid. Also, when d is endogenous with an instrument z, an instrumental-variables estimator (IVE) is often applied, again despite the invalid linear model. Furthermore, the treatment effect is likely to be heterogeneous, say, µ 1(x), not a constant as assumed in most linear models. Given these problems, the question is then what kind of effect the OLS and IVE actually estimate. Under some restrictive conditions such as a "saturated model", the estimated effect is known to be a weighted average, say, E { ω (x) µ 1(x)}, but in general, OLS and the IVE applied to linear models with a noncontinuous outcome or heterogeneous effect fail to yield a weighted average of heterogeneous treatment effects. Recently, however, it has been found that E { ω (x) µ 1(x)} can be estimated by OLS and the IVE without those restrictive conditions if the "propensity-score residual" d − E (d| x) or the "instrument-score residual" z−E (z| x) is used. In this article, we review this recent development and provide a command for OLS and the IVE with the propensity- and instrument-score residuals, which are applicable to any outcome and any heterogeneous effect. [ABSTRACT FROM AUTHOR]

This article presents a novel perspective on classification, utilizing the Spline nonparametric model as its foundation. In the realm of classification, this Spline model serves as a viable alternative to geographically weighted regression, logistic regression, and other classification models, especially when data patterns do not adhere to a specific distribution. Model parameter estimation is performed through the Ordinary Least Squares (OLS) approach, and the data poverty severity index is employed to assess the model's performance. The results indicate that the Spline model with three knots outperforms other models, thus establishing itself as the cornerstone for classification. Additionally, we provide an overview of the distribution map based on the derived mathematical model. [ABSTRACT FROM AUTHOR]

This study aimed to identify COVID-19 clusters in ASEAN countries using Global Moran's I. The results revealed an absence of spatial autocorrelation in the study area, indicating the non-existence of clusters in ASEAN countries during the 2021 pandemic. However, high-low (HL) and low-high (LH) outliers were identified in the Philippines, Brunei Darussalam, and Timor-Leste, respectively. Bivariate local Moran's I was employed to analyze cluster areas between the dependent variable (COVID-19 confirmed cases) and independent variables categorized as 1.) behavioral (Smoking rate (SMK) and Number of older people (65+) (OLD)), 2.) Healthcare (Number of cardiovascular patients (CAR) and Number of lung cancer patients (LNG)), and 3.) Socioeconomic factors: Total population number (TPN), Urban population (UBP), Population density (PDT), Human development index (HDI), GDP per capita (GDP), Life expectancy index (LEI), and International tourist arrival (ITA). The results showed that Indonesia, with high COVID-19 confirmed cases, was surrounded by high rates of independent variables. Bivariate local Moran's I can be instrumental in formulating disease control plans for individual countries. Correlations between COVID-19 and independent variables were modeled using Ordinary Least Squares (OLS), Spatial Lag Model (SLM), and Spatial Error Model (SEM). OLS was deemed less effective compared to the other models. Additionally, p-values from SLM and SEM indicated that only specific socioeconomic factors were associated with COVID-19 confirmed cases. The correlation of Smoking Rate (SMK) to the number of COVID-19 cases was questioned due to non-identical p-values from SLM and SEM. After excluding unassociated factors, SEM emerged as the best-fit model, boasting the highest R2 of 0.961 and the lowest AIC and BIC of 323.663 and 325.665, respectively, compared to other models. The correlation between COVID-19 confirmed cases and associated variables derived from SEM is COV = 204,763 + 0.017TPN - 710.617PDT + 54,967GDP + 340.898ITA. It is crucial to note that the correlations identified in this study do not imply a causal relationship between socioeconomics and COVID-19 infection in ASEAN countries. [ABSTRACT FROM AUTHOR]

This study attempts to understand the impact of electronic Word of Mouth (eWOM) on corporate financial performance during the COVID-19 pandemic. A supervised machine learning is used to determine the investors' sentiment of a news story (eWOM) towards a given company from a long position (buying) investors perspective. Ordinary Least Square (OLS) and dynamic quantile regression are used to test the role of eWOM on financial performance. Results reveal no significant relationship between eWOM and the firm's financial performance. Similarly, we do not find any evidence of an association between eWOM and corporate performance at different quantiles of financial performance. The findings contribute to the existing literature on eWOM and its impact on the financial performance during specific circumstances or financial crises. This study offers insights to researchers, policymakers, regulators, financial report users, investors, employees, clients, and society. [ABSTRACT FROM AUTHOR]

Developed countries are guaranteed to have the ownership of higher knowledge complexity, which is naturally accepted and universally acknowledged. However, the reality may tell a different story. In this article, a brand-new approach is utilized to quantify the knowledge complexity. Within the bipartite network model, based on the Fitness and Complexity algorithm and the matrix-estimation exercise, a couple of indicators are constructed to measure generalized knowledge complexities of countries and technologies. Results illuminate that admittedly knowledge complexities of countries and those of technologies are interrelated, and those established and developed countries, compared with developing ones, do not necessarily own higher knowledge complexity. To be more specific, an increasing number of facts demonstrate that some less-developed countries with less-advanced technologies are progressing in leaps and bounds, for they attach great importance to investing in high-knowledge-complexity technologies. [ABSTRACT FROM AUTHOR]

The observed low level of information and communication technology (ICT) usage among micro, small, and medium-sized enterprise (MSME) firms in many African countries may serve as a major constraint to their productivity. The productivity of service and manufacturing firms is impacted differently due to this low uptake of ICT. This paper seeks to examine the differential effects of ICT on MSMEs' productivity in emerging African economies, specifically Egypt and Nigeria. This study evaluates the effects of ICT on MSMEs' productivity across different sectors (services and manufacturing), by size and aggregated. The estimated results support the positive impact of MSMEs' website presence and use of email on their productivity, though the magnitude varies across different analyses and sectors. This appears to support the differential sectoral impacts of ICT on MSMEs' productivity across countries in emerging markets. Specifically, this study finds evidence for a higher impact of firms' website presence on productivity in the manufacturing sector than the services sector across emerging economies. The study, therefore, recommends that emerging markets economies that want to implement appropriate national technology policies to enhance their firms' productivity should always consider their differential effects on various sectors, as universal ICT policies may not achieve the desired objectives. [ABSTRACT FROM AUTHOR]

BackgroundIn low- and middle-income nations, a significant proportion of maternal and infant deaths are caused by a short birth interval (SBI). In Ethiopia, it is the main factor contributing to maternal and infant mortality. Understanding the spatial distribution of SBIs, i.e., birth intervals of less than 33 months, and the factors that influence them is important for categorizing and promoting targeted interventions. This study used a geographically weighted regression model to evaluate the factors associated with SBIs in hot areas of Ethiopia.MethodsThe 2019 Ethiopian Mini Demographic and Health Survey, which is nationally representative, provided the data for this study. The first step in the two-stage cluster design used to collect the data was enumeration areas, and the second stage was households. The survey was conducted between 21 March 2019 and 28 June 2019. A hot spot analysis (local Getis-Ord Gi* statistics) was initially used to investigate spatial variation in SBIs. Geographically weighted regression was used to examine the regional variation in the relationship between SBIs and the factors that cause them.ResultThe study indicated that the overall proportion of SBIs among women in Ethiopia was 43.2%. The values for Global Moran’s I (Moran’s I = 0.773 and p

Background Due to shocks and stressors brought on by both natural and man-made events, households that depend on subsistence agriculture frequently experience uncertainty about guaranteeing their food security. The modern strategy for achieving food security in the face of shocks depends on identifying the variables that affect resilience and working to increase rural households’ capacity to withstand shocks related to food insufficiency. The goal of this study was to evaluate how resilient households were to food insecurity and its determinants. Methods From primary and secondary sources, cross-sectional data of both quantitative and qualitative types were gathered. The data acquired through surveys from 320 households was analyzed using a multivariate analytical method that included principal component analysis (PCA) and OLS regression. To bolster the findings, data from focus group discussions (FGDs) and key informant interviews (KIIs) was substantially utilized. Results According to the findings of the analysis, 40% and 60% of households, respectively, become resilient and non-resilient. All but the stability parameter significantly impacted resilience. Resilience to food insecurity is significantly increased by an increase of 2.128, 1.697, 0.648, 0.363, and 0.158 units in the adoption of agricultural technology, asset ownership, access to essential services, and adaptive capacity, respectively. On the other hand, the stability dimension negatively impacts resilience, with each additional unit lowering households’ capacity for resilience by 0.155 units. Conclusions The study suggests that to reduce both short-term and long-term shocks and stresses of food insecurity and to increase the capacity for resilience, the government’s policies and programs and those of other development partners must focus on building the main components of resilience.

Abstract.This article gives large sample theory for the one component partial least squares estimator, including some hypothesis tests for high dimensional data, under much weaker conditions than those in the literature. Simple theory is also given for some data splitting estimators and the marginal maximum likelihood estimators. It is shown that lasso, one component partial least squares, and ordinary least squares often estimate different population multiple linear regression models. The article also proves that there are often many valid population models for regression methods such as binary regression. [ABSTRACT FROM AUTHOR]

This study addresses a fundamental, yet overlooked, gap between standard theory and empirical modelling practices in the OLS regression model y = X β + u with collinearity. In fact, while an estimated model in practice is desired to have stability and efficiency in its "individual OLS estimates", y itself has no capacity to identify and control the collinearity in X and hence no theory including model selection process (MSP) would fill this gap unless X is controlled in view of sampling theory. In this paper, first introducing a new concept of "empirically effective modelling" (EEM), we propose our EEM methodology (EEM-M) as an integrated process of two MSPs with data ( y o , X) given. The first MSP uses X only, called the XMSP, and pre-selects a class D of models with individually inefficiency-controlled and collinearity-controlled OLS estimates, where the corresponding two controlling variables are chosen from predictive standard error of each estimate. Next, defining an inefficiency-collinearity risk index for each model, a partial ordering is introduced onto the set of models to compare without using y o , where the better-ness and admissibility of models are discussed. The second MSP is a commonly used MSP that uses ( y o , X), and evaluates total model performance as a whole by such AIC, BIC, etc. to select an optimal model from D. Third, to materialize the XMSP, two algorithms are proposed with applications. • New concept and methodology of empirically effective modelling in regression. • Model selection procedure (MSP) controlling collinearity without dependent variable. • Inefficiency and collinearity controlling algorithms for individual OLSEs. • Decision-theoretic MSP exploring a class of models with collinearity controlled. [ABSTRACT FROM AUTHOR]

Findings reveal that the majority of studied areas are classified as bare lands, while the lowest amount is covered by lichens and mosses. Grassland and cropland occupy major areas of the region, with highest normalized difference vegetation index (NDVI) value saw in 2020, showing dense vegetation in the western, northwestern and northern regions. Afforestation efforts shown positive results, with a 4% increase in forested area between 2000 and 2022. [ABSTRACT FROM AUTHOR]

Given the role of Covid-19 pandemic in accelerating the digital transformation in Europe, the main aim of this paper is to explain the unemployment rate in Romania based on Internet searches for jobs during the epidemic at national and county level. Google Trends indexes for certain keywords related to jobs in Romanian language (’’locuri de muncă’’ and ’’joburi’’) and the most famous websites with job announcements (’’eJobs’’ and ’’Hipo’’) are considered. At national level, the unemployment rate in the period February 2020-December 2022 in Romania is explained using an autoregressive distributed lag model (ARDL) based on Google Trends index for ”locuri de munca”, while for youth unemployment ”joburi” and ”eJobs” are relevant. At county level, a spatial error model based on searches for ” locuri de munca” performs better than OLS regression. The results support the recommendations to improve governmental making-decision process. [ABSTRACT FROM AUTHOR]

This paper determines the impact of Brent oil shocks on the price of shares of companies from the oil, agricultural and food industries that includes the period of the COVID-19 pandemic. For this purpose, they use a quantile regression approach and compare its findings with a standard Ordinary Least Squares (OLS) regression model. Moreover, in this research they use quantile regression, which enables them to analyze different quantiles of share prices of companies from the oil industry, the agricultural industry, and the food industry. They observe three different periods - a period of recession, a normal period and a period of expansion. Finally, empirical evaluations using quantile regression and OLS models show us that shocks from the oil market are more pronounced in companies from the oil industry compared to companies from the agricultural and food industries. The findings of this research provide important information for investors, economic policy makers, and other parties. [ABSTRACT FROM AUTHOR]

This article explores the issue of spurious regression in econometric models, focusing on the case of imports in the United States. It explains that a high coefficient of determination (R2) without significant parameters can indicate the presence of spurious regression. The article warns that spurious regression can lead to misleading conclusions and offers recommendations for avoiding it in econometric models. It also discusses the importance of studying time series stability, the limitations of ordinary least squares (OLS) method, and the Gauss-Markov theory. Additionally, the article examines factors affecting imports and suggests methods to avoid spurious regression, such as following econometric methodology, using appropriate sample sizes, and utilizing modern econometric tools. It emphasizes the need for careful model specification, estimation, testing, and forecasting to prevent spurious regression. [Extracted from the article]

With the economy experiencing rapid growth in recent years, more individuals have started venturing into the stock market. Precisely forecasting the rate of return can mitigate investment risks for stock investors and significantly enhance their investment returns. The Capital Asset Pricing Model (CAPM) and the 3-factor Fama-French model (FF3) are widely recognized in academic and practical settings. This model comparison provides frameworks to analyze the relationship between portfolio risk and return in inefficient markets, contributing to applied data science in finance behavior. This research utilized the Support Vector Regression (SVR) algorithm to forecast the returns of a diversified portfolio in the Hanoi stock market (HNX) from 2010 to 2022. Initially, this study calculated the factors and subsequently constructed diversified portfolios. Subsequently, the explanatory power of the CAPM and FF3 models were compared using the Ordinary Least Squares (OLS) algorithm. Finally, this research incorporated the SVR algorithm within the FF3 framework to develop a predictive model. The research findings demonstrate that the FF3 model provides a superior explanation to the CAPM model. Additionally, the study reveals that the SVR algorithm outperforms the OLS algorithm in terms of efficiency, as it yields lower Root Mean Square Error (RMSE) values. Nevertheless, despite its advantages, the FF3 model still falls short regarding explanatory factors. Consequently, the next research direction entails replacing the FF3 model with a more comprehensive multi-factor model, anticipating obtaining an enhanced predictive model. [ABSTRACT FROM AUTHOR]

AbstractThe ordinary least square and ridge regression estimators are very sensitive to the joint presence of multicollinearity and outliers in the y-direction. The method of robust ridge regression with penalized or biasing parameter provides biased but efficient results than least square and ridge regression estimators. The optimal choice of km plays a key role in such a bias-variance tradeoff. In this paper, we have considered some existing estimators and also proposed some new penalized m-estimators. The new estimators have been compared with existing estimators through extensive Monte Carlo simulations. Based on the mean squared error criterion, the new penalized m-estimators outperform all the competing estimators under certain conditions. The Tobacco and Sports data sets have been used as numerical examples in the study. The findings show that the new penalized m-estimators outperform in the existing m-estimators. [ABSTRACT FROM AUTHOR]

The linear regression model explores the relationship between the dependent variable and the independent variables. The ordinary least squared estimator (OLSE) is widely applicable to estimate the parameters of the model. However, OLSE suffered a breakdown when the independent variables are linearly dependent- a condition called multicollinearity. The KibriaLukman estimator (KLE) was suggested as an alternative to the OLSE and some other estimators (ridge and Liu estimators). In this paper, we developed a Jackknifed version of the Kibria-Lukman estimator- the estimator is named the Jackknifed KL estimator (JKLE). We derived the statistical properties of the new estimator and compared it theoretically with the KLE and some other existing estimators. Theoretically, the result revealed that JKLE possesses the lowest MSE when compared with the KLE and some other existing estimators. Finally, JKLE reduced the bias and the mean squared error (MSE) of KLE in both simulation and real-life analysis. JKLE dominates other methods considered in this study. [ABSTRACT FROM AUTHOR]

Many freely available Digital Elevation Models (DEM) have increasingly been used worldwide due to the difficulty in acquiring accurate elevation data in some regions, emphasizing the need to investigate their accuracy and the factors that may influence their uncertainties. We performed an accuracy analysis of the Topodata DEM in the hydrographic region of Uruguay (Brazil) assuming that its vertical accuracy may be related to terrain characteristics. Multiscale Geographically Weighted Regression (MGWR) was applied to investigate the spatial scales over which terrain characteristics affect local variations in altimetric errors. MGWR outperformed Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). MGWR results also showed that aspect, curvature, and artificial areas operate at much smaller scales than elevation and have a higher influence in areas with high positive altimetric errors. The model explains about 41% of the total variation of the altimetric error of the Topodata DEM in the study area. Our findings enrich the understanding of the global and local processes affecting the accuracy of the Topodata DEM and shed light on the importance of local terrain characteristics in effective DEM product development. DEM products provide fundamental information for several research areas. OLS, GWR and MGWR were applied to identify the factors explaining the altimetric error of a DEM. MGWR investigated the spatial scales over which terrain characteristics affect local variations in altimetric errors. MGWR outperformed OLS and GWR proving that terrain characteristics operate at different scales. [ABSTRACT FROM AUTHOR]

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Treatment effects are often estimated by the least squares estimator controlling for some covariates. This paper investigates its properties. When the propensity score is constant, it is a consistent estimator of the average treatment effects if it is viewed as a semiparametric partially linear regression estimator, but it is not necessarily more efficient than the simple difference-of-means estimator. If it is literally viewed as a least squares estimator with a finite number of controls, it is equal to the weighted average of conditional average treatment effects with potentially negative weights, although the negative weight issue does not exist under semiparametric interpretation. It is shown that the negative weight issue can be avoided by use of logit specification. [ABSTRACT FROM AUTHOR]