Set Of Explanatory Variables Research Articles

Meeting Linnean, Wallacean, and Darwinian shortfalls in global biodiversity hotspots: A model study from the Indian Himalayan Region

AbstractDespite recent efforts to make large‐scale biodiversity datasets available, several data shortfalls still exist that preclude our progress in achieving global conservation and sustainability goals. In this study, we present a comprehensive native tree dataset (1689 species) from the Indian Himalayan Region (IHR)—home to two global biodiversity hotspots—assembled from an extensive data synthesis. Based on this database, we investigate the geographic patterns and drivers of α‐ and β‐taxonomic and phylogenetic diversity of the native trees among 13 different provinces of IHR. Our results revealed a considerable variation in the α‐ and β‐taxonomic and phylogenetic diversity among the provinces of IHR, with the highest values in eastern provinces. We found phylogenetic clustering mostly in the western provinces, and phylogenetic dispersion in the eastern provinces. We found a positive correlation between the taxonomic and phylogenetic dissimilarity across the IHR. Also, the different sets of explanatory variables explained the variation of tree species richness, standardized effect size of phylogenetic diversity, net relatedness index, and nearest taxon index, with maximum contribution by temperature seasonality (Bio4). Furthermore, temperature‐related climatic distance individually explained most of the variation in the taxonomic and phylogenetic dissimilarity between the provinces of IHR. Overall, our findings unveil the patterns of taxonomic, biogeographic, and phylogenetic dimensions of tree flora in the IHR, which in turn can help in formulating scientific data‐based regional policy and conservation strategies. Looking forward, we presented a model study for bridging the Linnean, Wallacean, and Darwinian shortfalls in the globally data‐deficient biodiversity‐rich regions.

The distribution of shared parking use in time and space: A case study in Guangzhou, China

Shared parking enables private parking owners to share their parking spaces during idle time. In recent years, increasingly more research investigated people’s intentions to participate in shared parking schemes as well as optimization algorithms to match shared parking supplies and demands. However, little research has investigated the distribution of shared parking use in time and space in implementation. To fill this gap, this study uses the transaction records of 121 shared parking lots in Guangzhou, China, and applies a quasi-Poisson regression model to analyze the influence of a set of explanatory factors on the total number of transactions. The results show that the number of parking transactions is significantly influenced by implemented duration, parking lot capacity, land use of shared facility, number of POIs (point of interest), and transit stations within a range of 750 meters from the shared parking lot. This study also applies a linear regression model to analyze the effect of a set of explanatory variables on the average parking duration at shared parking lots. The results show that the average parking duration is significantly influenced by the land use of a shared facility, number of office buildings within 750 meters from the shared parking lot, and peak time of the shared parking lots.

Exploring the concept of tourism competitiveness across the OECD countries

ABSTRACT The present study takes a comprehensive approach to investigate how a new set of untested determinants affects a destination's competitiveness across the OECD countries. Cross-section analyses were implemented for 2009, 2015 and 2021. We conceptualize destination competitiveness in terms of tourism's direct contribution to GDP and energy efficiency. We used two discrete and profitable tourism segments, investments and internal consumption in the travel and tourism industry as explanatory variables. To strengthen our scientific approach, we include the travel and tourism competitiveness index (ttci) in our specifications. This set of explanatory variables is scarce if not omitted in relevant tourism studies. Research findings indicate that investments in tourism lower energy consumption rates, whereas increases in the travel and tourism competitiveness index increase the direct contribution of tourism to a country's GDP. Moreover, as the tourism and travel competitiveness index increases, energy consumption decreases in all three reference years. Based on the results, practical implications indicate that policymakers and tourism planners should create a friendly and appealing investment environment, whereas energy efficiency should dominate every effort of further tourism development. This comprehensive approach instils confidence in the reliability and relevance of our findings for the tourism sector.

Direct and Indirect Mapping of Assessment of Quality of Life - 6 Dimensions (AQoL-6D) Onto EQ-5D-5L Utilities Using Data From a Multicenter, Cross-Sectional Study of Malaysians With Chronic Heart Failure

ObjectivesThe Assessment of Quality of Life - 6 Dimensions (AQoL-6D), a generic preference-based measure, is an appealing alternative to EQ-5D-5L for assessing health status in patients with chronic heart failure (HF), given its expanded scope. However, without a Malaysian value set, the AQoL-6D cannot generate health state utility values (HSUVs) to support local economic evaluations. This study intended to develop algorithms for predicting EQ-5D-5L HSUVs from AQoL-6D in an HF population. MethodsCross-sectional data from a multicenter cohort of 419 HF outpatients were used. Both direct and indirect mapping approaches were attempted using 5 sets of explanatory variables and 8 models (ordinary least squares, Tobit, censored least absolute deviations, generalized linear model, 2-part model [TPM], beta regression-based model, adjusted limited dependent variable mixture model, and multinomial ordinal regression [MLOGIT]). The models’ predictive performance was assessed through 10-fold cross-validated mean absolute error [MAE] and root mean squared error [RMSE]). Potential prediction bias was also examined graphically. The best-performing models, with the lowest RMSE and no bias, were then identified. ResultsAmong the models evaluated, TPM, which included age, sex, and 5 AQoL-6D dimension scores as predictors, appears to be the best-performing model for directly predicting EQ-5D-5L HSUVs from AQoL-6D. TPM yielded the lowest MAE (0.0802) and RMSE (0.1116), and demonstrated predictive accuracy for HSUVs >0.2 without significant bias. A MLOGIT model developed for response mapping had suboptimal predictive accuracy. ConclusionsThis study developed potentially useful mapping algorithms for generating Malaysian EQ-5D-5L HSUVs from AQoL-6D responses among patients with HF when direct EQ-5D-5L data are unavailable.

Bootstrap Approximation of Model Selection Probabilities for Multimodel Inference Frameworks.

Most statistical modeling applications involve the consideration of a candidate collection of models based on various sets of explanatory variables. The candidate models may also differ in terms of the structural formulations for the systematic component and the posited probability distributions for the random component. A common practice is to use an information criterion to select a model from the collection that provides an optimal balance between fidelity to the data and parsimony. The analyst then typically proceeds as if the chosen model was the only model ever considered. However, such a practice fails to account for the variability inherent in the model selection process, which can lead to inappropriate inferential results and conclusions. In recent years, inferential methods have been proposed for multimodel frameworks that attempt to provide an appropriate accounting of modeling uncertainty. In the frequentist paradigm, such methods should ideally involve model selection probabilities, i.e., the relative frequencies of selection for each candidate model based on repeated sampling. Model selection probabilities can be conveniently approximated through bootstrapping. When the Akaike information criterion is employed, Akaike weights are also commonly used as a surrogate for selection probabilities. In this work, we show that the conventional bootstrap approach for approximating model selection probabilities is impacted by bias. We propose a simple correction to adjust for this bias. We also argue that Akaike weights do not provide adequate approximations for selection probabilities, although they do provide a crude gauge of model plausibility.

Predicting hurricane evacuation behavior synthesizing data from travel surveys and social media

Evacuation behavior models estimated using post-disaster surveys are not adequate to predict real-time dynamic population response as a hurricane unfolds. With the emergence of ubiquitous technology and devices in recent times, social media data with its higher spatio-temporal coverage has become a potential alternative for understanding evacuation behaviour during hurricanes. However, these data are often associated with selection bias and population representativeness issues. To that extent, the current study proposes a novel data fusion algorithm to combine heterogeneous data sources from transportation systems and social media, in a unified framework to understand and predict real-time population response during hurricanes. Specifically, Twitter data of 2300 users are collected for evacuation response during Hurricane Irma and augmented behaviourally (probabilistically) with a representative National Household Travel Survey (NHTS) data, thus creating a hybrid dataset to improve the representativeness as well as provide a rich set of explanatory variables for understanding the evacuation behavior. The fusion process is conducted using a probabilistic matching method based on a set of common attributes across NHTS and Twitter. The fused dataset is employed to estimate the evacuation model and a comparison exercise is conducted to evaluate the performance of the model via fusion. The model fitness measures clearly demonstrate the improvement in data fit for the evacuation model through the proposed fusion algorithm. Further, we conduct a prediction assessment to illustrate the applicability of the proposed fusion technique and the results clearly highlight the improvement in the evacuation prediction rate achieved through the fused models. The proposed data-driven methods will enhance our ability to predict time-dependent evacuation demand for better hurricane response operations such as targeted warning dissemination and improved evacuation traffic management, allowing emergency plans to be more adaptive.

Bayesian effect selection in structured additive quantile regression

Bayesian structured additive quantile regression is an established tool for regressing outcomes with unknown distributions on a set of explanatory variables and/or when interest lies with effects on the more extreme values of the outcome. Even though variable selection for quantile regression exists, its scope is limited. We propose the use of the Normal Beta Prime Spike and Slab (NBPSS) prior in Bayesian quantile regression to aid the researcher in not only variable but also effect selection. We compare the Bayesian NBPSS approach to statistical boosting for quantile regression, a current standard in automated variable selection in quantile regression, in a simulation study with varying degrees of model complexity and illustrate both methods on an example of childhood malnutrition in Nigeria. The NBPSS prior shows good performance in variable and effect selection as well as prediction compared to boosting and can thus be recommended as an additional tool for quantile regression model building.

TOWARD SPATIAL AND SOCIAL EQUALITY: INVESTIGATING AND MODELING THE LINK BETWEEN OPPORTUNITY AND URBAN INTENSITY IN METRO MANILA, PHILIPPINES

Abstract. This paper employed an interdisciplinary mixed-method approach combining geomatics, geography, and sociology to investigate the link between opportunity and urban intensity across the 32 legislative districts of Metro Manila. Eighteen opportunity indicators, weighted within their opportunity dimensions using Analytic Hierarchy Process (AHP) were used to output Opportunity Scores, while annual VIIRS DNB nighttime light was used as proxy data to approximate the region’s Urban Intensity Score. Two regression models were developed with different sets of explanatory variables: an AHP-based model that used the top two weighted indicators from each opportunity dimension, and a model that used Exploratory Regression Analysis (ERA) to determine the appropriate variable combination. Modeling via Ordinary Least Squares Regression (OLS) produced a properly specified global model, while OLS modeling of the ERA-based model exhibited non-stationarity between variables which necessitated performing Geographically Weighted Regression (GWR). Our quantitative findings, combined with insights from our mini focus group discussion (FGD), provided evidence of the unequal distribution of opportunities across Metro Manila and the spatially varying relationship between urban intensity and opportunity, which have significant implications for urban framework as well as housing choice.

A firm-specific Malmquist productivity index model for stochastic data envelopment analysis: an application to commercial banks

In the data envelopment analysis (DEA) literature, productivity change captured by the Malmquist productivity index, especially in terms of a deterministic environment and stochastic variability in inputs and outputs, has been somewhat ignored. Therefore, this study developed a firm-specific, DEA-based Malmquist index model to examine the efficiency and productivity change of banks in a stochastic environment. First, in order to estimate bank-specific efficiency, we employed a two-stage double bootstrap DEA procedure. Specifically, in the first stage, the technical efficiency scores of banks were calculated by the classic DEA model, while in the second stage, the double bootstrap DEA model was applied to determine the effect of the contextual variables on bank efficiency. Second, we applied a two-stage procedure for measuring productivity change in which the first stage included the estimation of stochastic technical efficiency and the second stage included the regression of the estimated efficiency scores on a set of explanatory variables that influence relative performance. Finally, an empirical investigation of the Iranian banking sector, consisting of 120 bank-year observations of 15 banks from 2014 to 2021, was performed to measure their efficiency and productivity change. Based on the findings, the explanatory variables (i.e., the nonperforming loan ratio and the number of branches) indicated an inverse relationship with stochastic technical efficiency and productivity change. The implication of the findings is that, in order to improve the efficiency and productivity of banks, it is important to optimize these factors.

Evaluating the multi-variable influence on O3, NO2, and HCHO using BRTs and RF model

This study addresses significant knowledge gaps in understanding the complex interplay between atmospheric chemistry and synoptic conditions. Using emerging machine learning techniques—Boosted Regression Trees (BRTs) and Random Forest (RF) models—we investigate the influence of synoptic conditions on pollutant levels. Several BRTs and RF models are developed to estimate surface concentrations of ozone (O3), nitrogen dioxide (NO2), and formaldehyde (HCHO). By considering a range of algorithmic structures and explanatory variables for each pollutant, the research aims to identify the most skillful predictive approaches and influential factors governing pollutant levels. The design seeks to highlight key determinants of concentration patterns without constraining the investigation to pre-defined model structures or explanatory variable sets. Introducing a novel methodology, Correlation Coefficient Differential Evaluation (C2DE), we quantitatively assess the influence of explanatory variables. C2DE reveals significant contributions from spatial variables (i.e., trajectory clusters at varying altitudes), formaldehyde to nitrogen dioxide ratio (FNR), and meteorological parameters. Specifically, spatial variables contribute approximately 28 % to O3 concentrations, while the FNR accounts for around 5.2–9.8 % of the overall influence. For NO2 and HCHO, spatial variables contribute around 26.5 % and 32.1 %, respectively. Moreover, when considering the combined influence of meteorological parameters, these collectively explain about 45.34 %, 35.31 %, and 45.41 % of the variations in O3, NO2, and HCHO concentrations, respectively. Thus, C2DE provides valuable insights into the relative contributions of these factors, aiding in the comprehensive evaluation of air quality dynamics. This underscores the need for a multifaceted approach to comprehending and effectively addressing air pollution before devising its control strategies.

Bullying at School and Students' Learning Outcomes: International Perspective and Gender Analysis.

The present study empirically investigates how school-based bullying victimization affects students' learning outcomes, taking into consideration international and gender perspectives. The main objective of the present research is to provide a better understanding of the consequences of bullying victimization in the learning process of adolescents. We estimate a statistical function that empirically establishes the relationship between the students' outcomes in mathematics, reading, and science (output) and a wide set of explanatory variables (educational factors), one of which is that of being bullied. The present study uses a large sample of 612,004 students between 15 and 16 years old, attending 21,903 schools in 79 countries. The data come from the 2018 round of the Programme for International Student Assessment. The results indicate that bullying victimization is associated with decreases in academic achievement in mathematics, reading, and science. In addition, no relevant differences by gender are observed in reading and science but, other factors being equal, bullied males score less than bullied females in mathematics.

A study on the effectiveness of financial development on economic growth of Morocco

Economic growth’s theories suggest that a developed financial sector can promote the economy growth. However, the ability of financial sector to boost the economic growth depends on its ability to finance the economy. This paper investigates the relationship between Morocco's economic growth and financial development (FD). We base our study on the crucial role of banking sector in economic growth in developing countries through their financial intermediation. In order, to verify this relationship the vector error correction model (VECM) is used on secondary data: real GDP per capita (GDP) and a set of explanatory variables representing the financial development sector, specifically the ratio of private sector credit (CPS) and control variables including the share of government consumption (PC), the real interest rate (IR), trade openness (TO), and the inflation rate (INF). Our secondary data comes from the Central Bank of Morocco database and covers 56 quarters from 2007Q1 to 2020Q4. The findings support the long- and short-term benefits of financial sector development for economic growth in Morocco.

Comparative analysis of the X/Y/Z generations on the labor market in the European Union countries

Motivation: Today’s workplace is dominated by three generations: X, Y and Z. Each of them was formed in a different environment and is characterized by distinct needs and attitudes towards private and professional life. Identifying differences and similarities between generations and therefore adapting to generational change has become one of the main challenges of the modern labor market.Aim: The aim of this study was to determine what factors affect the level of involvement and well-being of representatives of generations X, Y and Z in the context of work, as well as to show the similarities and differences between generations in the context of the labor market in the European Union countries.Materials and methods: The study was based on the data from 2021 European Working Conditions Telephone Survey (EWCTS) providing information on job quality, work organization, work-life balance, health and well-being. To identify determinants of the level of commitment and well-being of employees, ordered logit models were used, based on the selection of the best set of explanatory variables and the conducted statistical analysis.Results: A comparative analysis of labor market metrics showed the differences between generations, primarily in terms of workplace support and levels of discrimination. The study shows that the level of engagement and well-being of employees from all generations is largely determined by the opportunity to utilize their skills and levels of job satisfaction and work-life balance. The most significant differences in the determinants of the level of engagement and well-being of employees from generations X, Y and Z concerned the level of trust towards employees and employers, the opportunity to learn new things and the discrimination experienced. The results obtained allowed for proposing several recommendations for employers.

Modeling Corporate CDS Spreads Using Markov Switching Regressions

Abstract This paper investigates the determinants of the European iTraxx corporate CDS index considering a large set of explanatory variables within a Markov switching model framework. The influence of financial and economic variables on CDS spreads are compared using linear, two, three, and four-regime models in a sample post-subprime financial crisis up to the COVID-19 pandemic. Results indicate that four regimes are necessary to model the CDS spreads. The fourth regime was activated during the COVID-19 pandemic and in high volatility periods. Further, the effect of the covariates differs significantly across regimes. Brent and term structure factors became relevant after the outbreak of the COVID-19 pandemic.

On the need to address fixed-parameter issues before applying random parameters: A simulation-based study

Count regression models have been applied to model expected crash frequency at individual roadway locations. Random parameters have been increasingly integrated into these models to account for unobserved heterogeneity. However, the introduction of random parameters might also mask issues in the model specification, leading to inaccurate relationships and model interpretation. Two of these specification-related issues are: (1) not considering the appropriate functional form of explanatory variables; and, (2) ignoring the best set of significant explanatory variables. To better examine the need for careful model specification, this study uses synthetic data to demonstrate that the consideration of random parameters does not address the two model specification issues identified. The results from the simulation study illustrate that (a) model specification issues cannot be circumvented by random parameters alone and (b) random parameter models including the exhaustive set of explanatory variables available offer significant model improvements.

Which individual-level factors explain public attitudes toward immigration? a meta-analysis

ABSTRACT Public attitudes toward immigration have attracted much scholarly interest and extensive empirical research in recent years. Despite a sizeable theoretical and empirical literature, no firm conclusions have been drawn regarding the factors affecting immigration opinion. We address this gap through a formal meta-analysis derived from the literature regarding immigration attitudes from the top journals of several social science disciplines in the years 2009–2019 and based on a population of 1185 estimates derived from 144 unique analyses on individual-level factors affecting attitudes to immigration. The meta-analytical findings show that two individual-level characteristics are most significantly associated with attitudes to immigration -- education (positively) and age (negatively). Our results further reveal that the same individual characteristics do not necessarily explain immigration policy attitudes and attitudes toward immigrants' contribution. The findings challenge several conventional micro-level theories of attitudes to immigration. The meta-analysis can inform future research when planning the set of explanatory variables to avoid omitting key determinants.

Spatio-Temporal Investigation of Public Transport Demand Using Smart Card Data

AbstractPolicymakers must find efficient public transport solutions to promote sustainability and provide efficient urban mobility in the course of urban growth. A growing number of research papers are applying Geographically weighted regression (GWR) to model the relationship between public transport demand and its influential factors. However, few studies have considered the rapid development of journey inference from ticket transaction data. Similarly, the potential of GWR to analyze spatio-temporal changes that reflect changes in transportation supply and thus provide a measure for evaluating the local success of transport supply changes has yet to be exploited. In this paper, we use inferred journeys from smart card inferences as the dependent variable and analyze how public transport demand responds to a set of explanatory variables, emphasizing transport supply. Consequently, GWR and its successor Multiscale Geographically Weighted Regression (MGWR) are applied to analyze the spatially varying impact of transport supply changes for seven consecutive time frames between autumn 2017 and spring 2020, allowing conclusions about local changes in transport demand, as well as the benchmarking of transport supply changes. The (M)GWR framework’s predictive power is evaluated by training the model with past transport supply data and testing the model with data from the following consecutive years. The conducted analyses reveal that the (M)GWR model, using inferred journeys and transport supply data, can retrospectively predict the impact of transport supply changes on travel behavior and thus provides conclusions about the success of transport policies.

Quantile Estimation Using the Log-Skew-Normal Linear Regression Model with Application to Children’s Weight Data

In this article, we establish properties that relate quantiles of the log-skew-normal distribution to its parameters, allowing us to investigate the relationship between quantiles of a positive skewed response variable and a set of explanatory variables via the log-skew-normal linear regression model. We compute the maximum likelihood estimates of the parameters through a correspondence between the log-skew-normal and skew-normal linear regression models. Monte Carlo simulations show the satisfactory performance of the quantile estimators. An application to children’s data is presented and discussed.

Impact of Company Income Tax Revenue on Economic Growth of selected West African States (an application of the Dynamic Panel ARDL)

The study seeks to investigate the impact of company income tax on economic growth in selected 15 West African countries using the panel ARDL from 2002-2021. Based on the outcome of the hausman test, the study chose the Pooled Mean Group (PMG) because the probability Value is greater than 5% level of significance. PMG estimator assumes only ' are same across group that is, only the long run estimators are the same or homogenous and short run estimators varies. The data are found to be stationary at first difference. And the correction result shows that there is no problem of multicollinearity. The study found out that there is a long run relationship between company income tax and economic growth. The ECT coefficient is negative for Nigeria, Ghana, and Sierra Leone, indicating that these countries converge to their long-run equilibrium at a rate of 0.9%, 95.2%, and 39.4%, respectively, per period. the short-run impact of various variables on the dependent variable for Group B countries, namely Senegal, Burkina Faso, Guinea Bissau, Guinea, and Cote d'Ivoire. The model appears to include a lagged dependent variable (ECT) as well as a set of explanatory variables including D.CIT, D.FDI, D.POP, D.FID, and D.TOP. with negative coefficient indicating that these countries converge to their long-run equilibrium. And same with group C. In conclusion, the impact of company income tax revenue on economic growth in selected West African states, as examined through the panel ARDL framework from 2002 to 2021, has been found to be significant. The study reveals that there is a positive and statistically significant relationship between company income tax revenue and economic growth in the selected West African countries. The study also found evidence of short-run dynamics and long-run equilibrium in the relationship between the two variables. This suggests that an increase in company income tax revenue can have a positive impact on the economic growth of these nations. The study recommended that, policymakers in selected West African states should adopt a multi-pronged approach that focuses on increasing company income tax revenue, promoting trade openness, attracting foreign direct investment, and improving human capital development. By doing this, they can ensure sustained economic growth and development in their countries.

Solar panel adoption among Mexican small and medium-sized commercial and service businesses

We analyze the determinants of adoption of distributed solar photovoltaic systems, focusing on small and medium-sized commercial and service firms. We use monthly billing data that are perfectly matched with data from a novel survey that gathers information on electricity consumption, stock of electric equipment, and a rich set of firm characteristics in the Metropolitan Area of Aguascalientes, Mexico. Using an econometric model, we find evidence that a set of explanatory variables such as business characteristics, the economic sector, ownership status, stock and usage of equipment and appliances, presence of other solar technologies, and views about the use of renewable energy are important determinants of the probability of adoption of solar panel systems. Furthermore, using machine learning methods to identify the best predictors of solar adoption, we indirectly validate the theory-driven empirical model by assessing a large set of explanatory variables and selecting a subset of these variables. In addition, we investigate relevant cases where a priori solar panel adoption seems to be cost-effective but structural adoption barriers and adoption gaps might coexist for certain groups of electricity users. We also calculate the social cost savings and the avoided CO2 emissions. Finally, based on our results, we provide several policy implications and recommendations.