Dependence Of Cross Section Research Articles

Tourism, Financial Development and Sectoral Development in ECOWAS Countries: Empirical Evidence from the CS-ARDL Approach

This study examines the role of financial development in the relationship between tourism and sectoral development in 12 ECOWAS countries over the period 2003-2020. Methodologically, it mobilized the Cross-sectional Augmented Autoregressive Distributed Lag (CS-ARDL) model developed by Chudik and Pesaran (2015), which takes cross-sectional dependence and heterogeneity into account. The results reveal that tourism hurts the development of the agricultural and industrial sectors in both the short and long term. Financial development harms the agricultural sector, while it improves the development of the industrial sector in both the short and long term. On the other hand, tourism associated with a developed financial sector improves the development of the agricultural sector and also the industrial sector. This result suggests that the contribution of tourism to sectoral development in ECOWAS countries depends on the level of financial development.

Environmental Governance and Gender Inclusivity: Analyzing the Interplay of PM2.5 and Women’s Representation in Political Leadership in the European Union

This research addresses a critical gap in the literature by establishing a direct correlation between particle pollution from fine particulates (PM2.5) and women’s political participation. It offers a nuanced understanding of gender dynamics in governance and their impact on environmental outcomes. Focusing on 27 European Union (EU) countries from 2013 to 2021, econometric techniques unveil common trends, underscoring cross-sectional dependence. This study reveals distinct gender behaviors in combating pollution, with women in parliaments and regional assemblies notably contributing to pollutant reduction. However, the negative impact of women’s engagement in politics on PM2.5 intensifies the lower part of the representation hierarchy. Policymakers are urged to create conditions fostering women’s political participation, advocating for gender quotas to address underrepresentation. The research emphasizes the interconnectedness of gender, politics, and environmental issues, urging heightened awareness among policymakers. Limitations include a confined scope and duration, requiring cross-validation beyond the EU. Future research pathways involve exploring the intricate connections between gender, politics, and environmental initiatives, seeking thresholds for impactful women’s representation. In contributing to Sustainable Development Goals (SDGs), this study aligns with SDG 5 (gender equality), SDG 3 (good health and well-being), SDG 7 (affordable and clean energy), SDG 11 (sustainable cities and communities), SDG 16 (peace, justice, and strong institutions), and SDG 17 (partnerships for the goals).

Analysing the Role of Globalisation in Environmental Degradation of West African Countries: A Method of Moments Quantile Regression Approach

Abstract Motivated by the need to achieve a sustainable environment as entrenched in the Sustainable Development Goals and the conflicting evidence in the literature, this study scrutinises the effect of different components of globalisation on the environmental degradation of West African nations within the Environmental Kuznets Curve framework. Fully modified ordinary least squares, dynamic ordinary least squares, and the method of moments quantile regression of Machado and Silva (2019) are deployed as the estimation techniques. In the panel, cross-sectional dependence and non-normality are established. The findings from the quantile regression reveal a significant positive impact of economic growth on environmental degradation. The square of economic growth has an insignificant negative influence on environmental degradation. Thus, the EKC hypothesis is not valid in West African nations. Aggregate globalisation significantly influences environmental degradation, while economic and political globalisation significantly negatively impacts environmental degradation across the quantiles. However, social globalisation negatively impacts environmental degradation at a lower quantile (10–50 %), while at a higher quantile, the impact is positive. Natural resources significantly affect environmental degradation, while energy consumption in the region benefits the environment. Nevertheless, urbanisation has a different impact on environmental degradation in the area. The results of these studies are robust to other methods of estimation. The implications of the study are discussed.

Does corruption moderate the military spending – Informal economy nexus? The empirical evidence from Asian countries

PurposeIn pursuit of good governance and better allocation of resources, corruption and informal economy are of interest to policymakers and citizens alike. The impacts of military spending on the informal economy are scant. Moreover, the effects of an external factor, such as corruption that moderates this relationship, have largely been neglected in previous studies. Hence, this paper investigates how corruption moderates the effects of military spending on the informal economy in 30 Asian countries from 1995 to 2017.Design/methodology/approachThis paper utilizes the GMM estimation technique, which allows cross-sectional dependence and slope homogeneity in panel data analysis, to examine the moderating role of corruption on the relationship between military spending and the informal economy.FindingsEmpirical findings from this paper indicate that an increase in military spending declines the informal economy while corruption increases it. Interestingly, the negative effects of military spending on the informal economy will mitigate with a greater degree of corruption in the Asian region. We also find that enhancing economic growth and attracting more FDI has reduced the informal economy in Asian countries.Originality/valueTo the best of the authors' knowledge, this is the first empirical study conducted to examine the moderating role of corruption on the military spending – informal economy nexus. Thus far, this approach has not been investigated in the existing literature, particularly for Asian countries.

An Interpretable and Efficient Infinite-Order Vector Autoregressive Model for High-Dimensional Time Series

As a special infinite-order vector autoregressive (VAR) model, the vector autoregressive moving average (VARMA) model can capture much richer temporal patterns than the widely used finite-order VAR model. However, its practicality has long been hindered by its non-identifiability, computational intractability, and difficulty of interpretation, especially for high-dimensional time series. This article proposes a novel sparse infinite-order VAR model for high-dimensional time series, which avoids all above drawbacks while inheriting essential temporal patterns of the VARMA model. As another attractive feature, the temporal and cross-sectional structures of the VARMA-type dynamics captured by this model can be interpreted separately, since they are characterized by different sets of parameters. This separation naturally motivates the sparsity assumption on the parameters determining the cross-sectional dependence. As a result, greater statistical efficiency and interpretability can be achieved with little loss of temporal information. We introduce two l 1 -regularized estimation methods for the proposed model, which can be efficiently implemented via block coordinate descent algorithms, and derive the corresponding nonasymptotic error bounds. A consistent model order selection method based on the Bayesian information criteria is also developed. The merit of the proposed approach is supported by simulation studies and a real-world macroeconomic data analysis. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.

The Determinants of Prices of Fan Tokens as a New Sports Finance Tool

In the Covid-19 era, the income sources of sports clubs decreased, but the importance of fan tokens as new communication, management, and income source for sports clubs increased with Blockchain technology. For this reason, the relationships between fan token prices and club success, transaction volume, and attractiveness factors were examined and the important determinants of fan token prices were tried to be revealed. To determine the most suitable methods for panel data model, first of all, cross-section dependence, homogeneity, and unit root tests were carried out. Then the durbin test was performed and it was concluded that the variables were cointegrated. Finally, the coefficients were examined with the PMG/ARDL test and it was observed that the most important determinants of fan token prices were club success and transaction volume, respectively. While these factors affect fan token prices positively, the effect of the attractiveness factor is seen as less important and negative compared to other factors. In addition, in the error correction model established, the existence of a long-term equilibrium relationship between the variables was confirmed. It was concluded that the short-term deviations returned to the equilibrium after approximately 21 days.

Environmental outlook of ASEAN-5 through the lens of green bonds, environmental technologies and financialization

Sustainable development goals call for urgent action with an aim to spur socio-economic growth along with the protection of the environment and resources. However, these goals are difficult to be achieved when finances are not mobilized to mitigate harmful resources. In return, numerous green bond manifestations appeared in the market, which are viewed as a bridge to have successful implementation of sustainable development goals. Therefore, the study is aimed to explore the relevance of green bonds, financialization, and sustainable environmental change mitigation technologies for environmental change in ASEAN economies. The initial outcomes confirm the long-run cointegration association between study variables and also explore the slope heterogeneity and cross-sectional dependencies. To explore the causal connections between the study variables, the current study employs CS-ARDL technique. The study finds significant contributions of sustainable environmental change mitigation technologies and green bonds in the reduction of pollution, hence improving climate quality. The short-run infers similar results with relatively lower magnitudes of coefficients as compared to the long-run. Negative error correction terms are used to confirm the stability of the model, which implies sustained convergence towards “steady-state equilibrium” in case of expected deviations. The results offer practical implications and recommendations.

Cosmological and idealized simulations of dark matter haloes with velocity-dependent, rare and frequent self-interactions

ABSTRACT Dark matter self-interactions may have the capability to solve or at least mitigate small-scale problems of the cosmological standard model, Lambda cold dark matter. There are a variety of self-interacting dark matter models that lead to distinguishable astrophysical predictions and hence varying success in explaining observations. Studies of dark matter (DM) density cores on various mass scales suggest a velocity-dependent scattering cross-section. In this work, we investigate how a velocity dependence alters the evolution of the DM distribution for frequent DM scatterings and compare to the velocity-independent case. We demonstrate that these cases are qualitatively different using a test problem. Moreover, we study the evolution of the density profile of idealized DM haloes and find that a velocity dependence can lead to larger core sizes and different time-scales of core formation and core collapse. In cosmological simulations, we investigate the effect of velocity-dependent self-interaction on haloes and satellites in the mass range of ≈1011–$10^{14} \, \mathrm{M_\odot }$. We study the abundance of satellites, density, and shape profiles and try to infer qualitative differences between velocity-dependent and velocity-independent scatterings as well as between frequent and rare self-interactions. We find that a strongly velocity-dependent cross-section can significantly amplify the diversity of rotation curves, independent of the angular dependence of the differential cross-section. We further find that the abundance of satellites in general depends on both the velocity dependence and the scattering angle, although the latter is less important for strongly velocity-dependent cross-sections.

Empirical evidence of emissions discourse related to food, beverage, and tobacco production in leading manufacturing nations.

The proliferation of carbon dioxide (CO2) emissions has resulted in significant environmental challenges worldwide, particularly within industrialized nations. The present paper tries to investigate the role of food, beverages, and tobacco in mitigating CO2 emissions in the top industrial countries. Economic growth and renewable and non-renewable energy consumption are integrated as control variable in the empirical model. The study applied the pooled mean group-autoregressive distributed lag (PMG-ARDL) model proposed by (J Appl Econ, 16:289-326, 2001) and Granger causality test for causalities directions. The empirical outcomes suggest the presence of cross-sectional dependence, and variables are integrated of order one and cointegrated. Long-run estimates revealed the presence of inversed U curve proving the validity of the environmental Kuznets curve. Also, the results show that renewable energy (RE) consumption contribute in reducing emissions, while non-renewable energy (NRE) and food, beverages, and tobacco (FBT) led to increase emissions level in the long run. Granger shows bidirectional long-run relationship between CO2 emissions, non-renewable energy, and economic growth. Moreover, Granger recorded no causality between food, beverages, and tobacco and any other variable. We recommend that firms engaged in the production of food, beverages, and tobacco products are increasingly urged to make investments in clean technologies powered by renewable energy sources for their manufacturing processes. This is considered a necessary step to achieve a significant reduction in CO2 emissions over an extended period.

Improved inference for interactive fixed effects model under cross-sectional dependence

Improved inference for interactive fixed effects model under cross-sectional dependence

Permanent VS Transitory Shocks to Electricity Consumption: Panel Evidence from 19 African Countries

ABSTRACT: Considering Africa's increasing electricity demands and the pivotal role of electricity in socio-economic development, this study addresses a research gap in understanding the stochastic properties of per capita electricity consumption in African contexts. Through examining the integration properties of per capita electricity consumption across 19 African countries, the research determines whether 'shocks' yield transitory or permanent effects, aiding policy formulation and future demand forecasting. Annual electric power consumption data (1971 – 2014) (kWh per capita) from the WDI across 19 African countries are used. Our methodology starts with formally testing for cross-sectional dependence (CSD) using various tests to avoid potential size distortions. Subsequently, we employ the novel panel stationarity test of Nazlioglu and Karul (2017), a Fourier KPSS panel stationarity test. The robustness of the analysis is enriched by incorporating the Hadri and Kurozumi (2011) panel stationarity test and Nazlioglu et al. (2021) new panel stationary tests. The CSD test results reveal mutual dependency in electricity consumption among the sampled African countries, underscoring the need for a panel testing methodology that accommodates CSD. The results of the panel stationarity tests that allow CSD and structural breaks—specifically, the Nazlioglu and Karul (2017) test, which models structural breaks as a gradual process—reject the null hypothesis of trend stationarity for both single frequencies, i.e., k =1, and 2. Similarly, all new, complementary panel stationarity tests from Nazlioglu et al. (2021) reject the null hypothesis of stationarity for both frequencies. Collectively, the outcomes from various panel stationarity tests present strong evidence of nonstationary per capita electricity consumption within the sampled African countries. Additionally, the Nazlioglu and Karul panel stationarity test enables an analysis of the individual Fourier KPSS test for cross-sectional units in the panel. Of the 19 countries, only Togo does not reject the null hypothesis of stationarity for both frequencies. These individual country results align with the panel outcomes, providing compelling evidence for the nonstationarity of the per capita electricity consumption series across the investigated African nations. Accordingly, shocks to electricity consumption will have permanent effects, indicating that per capita electricity consumption will not return to its pre-shock equilibrium path. In this case, demand or consumption stabilizing policies will have long-run effects.

Demographic transition and inflation

We propose a few testable hypotheses to examine the impact of demographic transition on inflation. We contribute to the existing literature by refining the formulation of hypotheses and treatment of slope heterogeneity and cross-sectional dependence by implementing the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) procedure that adopts dynamic common correlated predictors. To this end, we consider two panels consisting of eight advanced economies (AEs) and sixteen emerging market economies (EMEs) to gain insights into the current asymmetric global demographic transition. Our comprehensive analysis reveals that the share of the working-age population is inflationary in AEs and disinflationary in EMEs. This analysis suggests that an asymmetry in demographic transition between AEs and EMEs influences inflation differently. Our findings offer clues to policymakers regarding the influence of the cohort size of the prime and young working-age population on inflation. Specifically, policymakers in emerging markets should incorporate information about the changing structure of demographic variables as the asymmetry in transition can lead to varying impacts on inflation compared to the impacts in advanced nations.

Deconvolution of spatially averaged shock wave forms

Noncontact optical methods (e.g., shadowgraphy, schlieren, and interferometry) allow visualization of propagating shock waves, avoid sensor-field interaction, and retain the potential for very high bandwidth. The heterodyne Mach-Zehnder interferometer produces quantifiable noncontact samples of the accumulated phase shifts induced by a shock wave, as viewed through its beam projection. This instrument is commercially available as a Laser Doppler Vibrometer (LDV). For shocked wave fields in air obeying spherical symmetry, point values for density and overpressure can be inferred. An important shortcoming for inference quality is the finite spatial dependence of the probe beam cross section, the profile shape itself serving to limit both the spatial resolution and the measurement bandwidth. We propose to model both of these effects of spatial averaging by constructing a model waveform and a model beam profile, convolving them, and comparing the result to synthesized LDV output. Mismatch between the convolved result and spatially averaged measurement can be quantified by a cost function, which is minimized via gradient descent.

Hysteresis in Youth Unemployment in Sub-Saharan Africa: Focus on Gender, Income, and Region

ABSTRACT: Despite innumerable government policies to reduce youth unemployment in Sub-Saharan Africa, the problem persists. This study examines the extent to which the hysteresis effect exists in the youth unemployment rate in Sub-Saharan African countries during the period 1991 to 2021, disaggregated according to territorial delineation, income, and gender. First, we employ the univariate and panel unit root tests including the Augmented-Dickey-Fuller, Phillips-Perron, the Dickey-Fuller-Generalized Least Square by Elliott, Rothenberg, and Stock (1996), the ADFmax by Leybourne (1995), and the Kwiatkowski, et al. (1992) test, to enable comparison with previous studies. The Pesaran's (2007), test is then applied to account for cross-sectional dependence. Finally, the Zivot–Andrews single structural break by Zivot and Andrews (1992), Clemente–Montane–Reyes two structural breaks unit root tests by Clemente, Montañés, and Reyes (1998), as well as the Karavias-Tzavalis panel unit root test with structural breaks by Karavias and Tzavalis (2014) are employed. The univariate unit root tests generally support the hysteresis hypothesis (within a band of 60% to 97%) for almost all countries, and sub-regions in SSA. However, accounting for structural breaks, the hysteresis effect in total youth unemployment is only visible in 19 countries (40%), with evidence of gender disparities pointing to the hysteresis effect in females only in 16 countries (34%), and, in males only in 10 countries (21.3%). The lower-middle-income countries reject the natural rate hypothesis in total and both male and female youth unemployment rates. While only male youths suffer from the hysteresis in low-income countries, the total youth in upper-middle-income countries shares this effect. Accounting for cross-section dependence alters the finding of the hysteresis effect only in males in Southern Africa and upper-middle-income countries. A one-size-fits-all policy may not work. Instead, we recommend an integral approach aimed at the creation of employment opportunities for youth and females, including but not limited to diversification of economic activities in rural areas to reduce urbanization, skills training, political and economic stability, cultural transformation to remove unnecessary gender-related employment discrimination, as well as improved economic integration policies. For countries found to exhibit the natural rate hypothesis, it may not be necessary to engage in costly policies to return the youth unemployment rate to equilibrium since the shock is only temporary.

Nexus among Exchange Rate Volatility, Inflation, and Economic Growth: A Panel Data Analysis

This study undertakes an examination of the intricate relationships among exchange rate fluctuations, inflation, and economic growth within South Asian nations, namely Pakistan, India, Bangladesh, Sri Lanka, and Nepal. Spanning the temporal horizon from 1990 to 2021, the research endeavors to unveil the repercussions of exchange rate instability on economic growth—a formidable challenge confronted by monetary authorities and policymakers in South Asia. The central focus lies on comprehending the dynamics of exchange rate volatility, inflationary pressures, and their collective impact on economic growth. In the pursuit of these objectives, GDP per capita serves as the yardstick for economic growth, while independent variables encompass a spectrum including broad money supply, exchange rate volatility, inflation, government consumption expenditures, and gross fixed capital formation. The quantification of exchange rate volatility entails the utilization of both the standard deviation and the GARCH (1,1) models. Subsequent to an in-depth analysis, the initial unit test results undergo reevaluation, incorporating the Breusch-Pagan LM, Pesaran CD, and Pesaran scaled LM tests to discern cross-sectional dependence. To address potential data non-stationarity while considering cross-sectional dependence, the Pesaran-CIPS and Pesaran's CADF tests are applied. A pivotal aspect of the research involves the application of a Feasibly Generalized Least Squares (FGLS) regression model. The outcomes of the FGLS panel data regression illuminate a noteworthy finding: exchange rate volatility, quantified through both techniques, significantly and adversely influences economic growth. In contrast, inflation exhibits a statistically insignificant negative impact on economic growth. The study culminates by proffering specific recommendations to policymakers, urging concerted efforts to ensure exchange rate stability and foster sustained economic growth.

Molecular processes for radiosensitizer compounds upon electron interactions

This work presents a theoretical analysis of electron impact elastic and inelastic processes for oxygen mimic and fluorinated radiosensitizer molecules over a wide energy range, spanning from ionization potential (IP) to 5 keV. The computation to determine elastic (Qel), inelastic cross sections (Qinel) and total cross sections (QT) is done through the Spherical Complex Optical Potential (SCOP) formalism and ionization cross sections (Qion) and summed excitation cross sections (∑Qexc) are deduced through the Complex Scattering Potential ionization contribution (CSP-ic) formalism. Additionally, a recently developed an alternative approach for complex and large molecules (55≤Z≤95), namely the Two Parameters Semiempirical Method (2p-SEM), for impact energy 50 eV to 5 keV is employed to estimate Qel and QT. These radiosensitizer molecules, imidazole, 2-nitroimidazole, 2-nitrofuran, 4(5)-nitroimidazole, pentafluorobenzoic acid, perfluorothiophenol are of particular interest due to their applications in cancer therapy treatment. Cross sections allow us to assess the probability of various molecular Processes occurring due to electron interaction. This work is the first report of the estimation of this electron driven molecular processes for these radiosensitizers except for imidazole and 2-Nitroimidazole where previous Qion results are available. Our Present results are in good accordance with available data from existing literature. The study incorporates various correlations to show size dependency of various cross sections and linear correlation of Qion vs. impact energy, Qel vs. impact energy, QT vs. impact energy and linear correlation of Qion (Max) vs. αI to confirm the credibility and consistency of the present results.

Reduced Rank Spatio-temporal Models

To simultaneously model the cross-sectional dependency and dynamic time dependency among n units, most research in spatial econometrics parameterizes the coefficient matrices among the n units as functions of known weights matrices. This modeling framework is over-simplified and faces the risk of misspecification when constructing the weights matrices. In this article, we propose a novel reduced-rank spatio-temporal model by assuming the coefficient matrices follow a reduced-rank structure. This specification avoids construction of the weights matrices and provides a good interpretation, especially for financial data. To estimate the unknown parameters, a quasi-maximum likelihood estimator (QMLE) is proposed and obtained via the Gradient descent algorithm with Armijo line search. We establish the asymptotic properties of QMLE when the number of units and the number of time periods both diverge to infinity. To determine the rank, we propose a ridge-type ratio estimator and demonstrate its rank selection consistency. The proposed methodology is illustrated via extensive simulation studies. Finally, a Chinese stock dataset is analyzed to investigate the cross-sectional and temporal spillover effects among stock returns.

Sea Ice Detection Method Using the Dependence of the Radar Cross-Section on the Incidence Angle

The method for sea ice detection using the data from the Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) satellite data is suggested. The approach is based on the analysis of the shape of normalized radar cross-section dependence on the incidence angle. The coefficient of kurtosis of surface slopes probability density function is introduced as a parameter to distinguish between open water and ice cover. The approach was validated using the data on sea ice concentration from the AMSR-2 radiometer in the Antarctic region.

Climate change and food security in South Asia: the importance of renewable energy and agricultural credit

Weather, trade restrictions, rising oil prices, a lack of financial support for farmers, and other factors have contributed to the destabilization of South Asian food security. The purpose of this study is to determine the long-run and short-run relationships between climate change, agricultural credit, renewable energy, and food security for a sample of South Asian countries between 1990 and 2021. The Dynamic Common Correlated technique is utilized for empirical analysis since it directly addresses the issue of cross-sectional dependency while delivering accurate cointegration findings. The study’s empirical findings show that climate change reduces food availability and increases the incidence of food insecurity in South Asia. In contrast, the use of renewable energy sources has a positive effect on food security in the short-run but not in the long-run, while the availability of credit to farmers has a positive effect on food security. Findings suggest that South Asian countries may reduce climate change’s negative effect on food security by investing in climate services, climate-resilient infrastructure, growing drought-resistant crops, using supplemental reinforced agricultural practices, and improving their weather forecasting capabilities.

Strategy towards sustainable energy transition: The effect of environmental governance, economic complexity and geopolitics

The Paris Agreement and COP27 have been actively working towards a transition to clean energy (SDG-7) and the restoration of the green environment (SDG-13). Therefore, this study was situated within a comprehensive policy framework. This study aims to investigate the effects of environmental governance and economic complexity on energy transition in 20 OECD countries selected for analysis from 1990 to 2021. This study employs the novel MMQR model to account for slope heterogeneity and cross-sectional dependency. Additionally, an asymmetric analysis was conducted to examine the mediating and moderating roles of geopolitical risk in the relationship between environmental governance, economic complexity, and energy transition. The primary findings of this study indicate that (1) environmental governance and economic complexity have a stimulating effect on energy transition at different levels of quantiles. Strict environmental policies have played a critical role in the transition to clean energy. Furthermore, the interaction between environmental governance and geopolitical factors negatively impacts energy transition at various quantiles; (2) economic complexity demonstrates a positive association with energy transition, as countries with high economic complexity possess the necessary resources, capabilities, and resilience to effectively address the challenges and seize the opportunities associated with transitioning to cleaner and more sustainable energy sources. However, the interaction of economic complexity with geopolitics transforms the positive influence of geopolitics into a negative influence on energy transition. The novel nonparametric panel Granger causality test establishes a significant causal relationship, revealing that environmental governance and economic complexity can support energy transition by creating a favorable environment for clean energy adoption, fostering innovation, facilitating effective planning and implementation, enhancing economic resilience, and promoting international collaboration.