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This study's primary goal was to explain how Ethiopia's economic growth affected government spending. The time series data utilized in the study were gathered between 1980 and 2018. The time series data were subjected to the Johansen cointegration test and the vector error correction model (VECM) in order to evaluate the short- and long-term correlations between public spending and economic growth in Ethiopia. According to the study, both long- and short-term economic growths are positively and significantly impacted by government spending on education. Long-term economic growth is negatively impacted by government expenditure on agriculture, while short-term effects are negatively impacted and considerable. In the long run, investment spending has a positive but negligible impact on economic growth; however, in the short run, it has a negative but large effect. Defense spending by the government has a positive and negligible effect on economic growth over the short and long terms. Both in the short and long terms, spending on health has a favorable and considerable impact on economic growth. According to the study, government spending on the education sector would help to foster the conditions that could result in higher labor force participation rates and, consequently, higher rates of economic growth. Aiming to establish a healthy and productive society that promotes economic progress, policy should focus on complementary measures to scale-up initiatives in the health sector. [ABSTRACT FROM AUTHOR]

In order to analyze the relationship between energy consumption and economic growth dynamics, the author proposes a dynamic analysis technology of energy consumption and economic growth based on the PVAR model. This technology uses the PVAR method to compare and quantitatively describe the relationship between economic growth and energy consumption in developed and developing countries from 1990 to 2009, using impulse response functions and variance decomposition analysis methods; study the similarities and differences between total energy consumption and various fossil energy consumption and the dynamic impact of economic development, and finally establish a PVAR model for analysis. Experimental results show that: in the forecast of variance analysis, the impact explanatory power of the total energy consumption of developing countries on the fluctuation of economic growth reaches 8.85070. However, the contribution of total energy consumption in developed countries to economic growth is insufficient. The explanatory power of economic growth in developed countries to total energy consumption is 15.58070, while that in developing country is 29.28070, both of which are greater than the explanatory power of their respective total energy consumption on economic growth. Conclusion. The technology based on the PVAR model can effectively meet the needs of analyzing the dynamic relationship between energy consumption and economic growth. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to reveal the mechanism and process of the dynamic evolution of the economy-energy-environment (EEE) system and study the variables that determine the speed of system evolution and development. The innovation is to overcome the defect of single index by constructing a system evaluation index and reveal the regional EEE system's evolution mechanism based on Haken's synergetic theory to supplement a systematic perspective's empirical evidence. It selects 30 provinces, municipalities, and autonomous regions in China to empirically analyse the evolution mechanism of the EEE system from 2005 to 2019. The empirical results show that economic development is the order parameter of the evolution and development of the EEE system. In the process of system evolution, the economic development subsystem is the decisive factor in EEE system evolution. In terms of the positive feedback mechanism of order parameter combination, the EEE system forms a positive feedback mechanism for increasing economic development in the stage of evolution and development. The negative feedback mechanism of decreasing environmental pollution emissions is formed in the EEE system. The increase in pollutant emissions has a certain inhibitory effect on economic growth. The improvement in the economic development level has no promoting effect on pollutants, and economic development, in turn, reduces environmental pollution. [ABSTRACT FROM AUTHOR]

We model a mixture of asymmetric and nonlinear bidirectional and unidirectional causality between four macroeconomic variables (exchange rate, GDP, global economic policy uncertainty, and relative CPI) and stock returns of BRICS economies in the frequency-domain using the information flow theory. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based Rényi effective transfer entropy approach is used to establish dynamic flow of information between macroeconomic variables and stock returns of BRICS. The original return series suggested insignificant information flow between most macroeconomic variables and stock returns. However, we reveal both asymmetric and tail dependent analyses at diverse scales between macroeconomic variables and stock returns of BRICS economies. Moreover, we find negative significant flow of information between the variables, in that knowing the history of one variable (either stock or macroeconomic variable), in this case, indicates considerably more uncertainty than knowing the history of only the other variable (either stock or macroeconomic variable). We also observe that global economic policy uncertainty has the most significant adverse causal relationship with stock returns of BRICS, especially in the long term. These results have important implications that investors and policymakers should take into account. Regulators should consider instituting sound policy actions geared towards minimising long-term effects of external shocks and uncertainties. [ABSTRACT FROM AUTHOR]

This paper introduces a method of combining phase space reconstruction and symbolic dynamics to study the causality between urbanization and economic growth at different regional levels in Shandong Province and finds that there is a strong positive relationship between urbanization and economic growth from China, indicating that the development of urbanization can drive the economic growth. Then, according to the results of correlation analysis between respective subvariables belonging to urbanization and economic growth and the principle of "deleting strong and reserving weak," the paper selects the independent variable and dependent variable to explore the hidden causal mechanisms that drive the developing of urbanization and economic growth from China. The results show that (1) the pattern causality between the independent variable and the dependent variable is dominated by dark causality at the provincial level; (2) the pattern causality between the independent variable and the dependent variable is dominated by dark causality at the Jinan economic circles and the Lunan economic circles, but the positive causality is dominated at the Jiaodong economic circles; (3) the types of causality between the same evaluation index and PU in different regions are different, and furthermore the degrees of positive, negative, and dark causality are different at two levels and three regions. The conclusion shows that although there is an obvious positive interaction between urbanization and economic growth, the influences of many factors are neither positive nor negative causality, but dark causality, which plays an important role in developing urbanization and economic growth. This work is useful for studying the law of causality between urbanization and economic growth, and this interesting result can be extended to other economic events. [ABSTRACT FROM AUTHOR]

In recent years, the level of local economic, social, and financial development had been greatly improved. However, due to the large gap between regions, the economic and financial industries in underdeveloped regions restricted the further development of the whole national economy to a great extent. It was known from the existing research that economic development was inseparable from the support of the financial industry, but the role of the financial industry in the process of regional economic development had not attracted attention. Therefore, from the perspective of economic development in underdeveloped areas, this paper put forward relevant assumptions by analyzing the relationship between economic growth and financial factors. In view of the development of financial industry in underdeveloped areas, this paper mainly analyzed from three aspects: banking, securities, and insurance, and selected corresponding indicators to observe the changes of banking, security, market and insurance market. Vector autoregressive model and vector error correction model were used to carry out cointegration test and Granger causality test for financial and economic-related indicators in underdeveloped areas. According to the impulse response function and variance decomposition results, this paper analyzed the dynamic relationship between financial development and economic growth, as well as the disturbance and duration of financial factors on economic development. The empirical results showed that the VAR model can better analyze the relationship between financial growth and economic growth, as well as the role of financial factors in the process of economic development. This study can provide reference for formulating financial development policies suitable for economic development. [ABSTRACT FROM AUTHOR]

A significant corridor under the "Belt and Road Initiative" is announced by the government of China for numerous transport and energy projects. China focuses greatly on the sustainability of these projects, in accordance with Green Investment Principles. For this, environmental, social, governance, and technological considerations are taken into account. This paper offers a novel assessment of the impact of green sustainability under BRI and the current emission scenario in Pakistan. The question that needs to be addressed here is whether the projects under Belt and Road Initiative are as environment friendly as they claim. In the first phase of this study, we review theories establishing evidence based on existing literature to determine the influence of Belt and Road on economic development. In the next phase, we use Environment Kuznets Curve and apply estimates from Johansson's Cointegration Test to quantify the present environmental scenario of Pakistan and probable environmental risk portrayed by the China-Pakistan Economic Corridor (CPEC). This paper helps in determining the relationship between carbon emission and three major variables including energy consumption, economic growth, and foreign direct investment (FDI). Cointegration analysis is applied on time series data comprising of a 40 years' period from 1979 to 2019. The paper finds that (i) the belt and road corridors could substantially improve trade, foreign investment, and living conditions for citizens in participating countries; (ii) moreover, this study confirms the presence of one cointegrated equation suggesting the presence of a long term relationship between the CO2 and independent variables and a short run association running from EnC to CO2. The study also proposes a Green BRI Model which formulates a body that devises environmental performance standards, reviews procedures, and ensures that they get implemented on all CPEC projects. The Belt and Road Initiative increases emissions among participating countries. China and corridor economies need to adopt strict policy reforms that increase transparency, expand trade, improve debt sustainability, and mitigate environmental, social, and corruption risks. [ABSTRACT FROM AUTHOR]

Economic development leads to the widespread use of energy, which results in carbon emissions. In order to determine the correlation between different urban carbon emissions and fiscal growth, a coupling and coordination model of urban carbon emissions and economic growth based on discrete Fourier transform (DFT) is constructed. According to the coupling and coordinated development characteristics of economic growth and carbon emissions, the evaluation index system of the regional economy and carbon emissions is constructed. This paper examines the proportion of primary energy consumption and the influencing factors of carbon emission in a city and constructs the economic growth computational intelligence model and urban carbon emission model. The coupling degree among economic growth, carbon emission, and energy consumption is studied; the coupling standard between economic growth and energy consumption carbon emission is determined; and the carbon emission factor under the method of DFT is introduced. The coupling coordination model between economic growth and carbon emission is constructed, and the interaction mechanism between carbon emission, economic development, and environmental protection is determined. The experimental findings demonstrate that when energy consumption intensity and carbon emission are relatively low, the model's change in the trend of coupling coordination index is comparable with the real/actual condition and the model is more reliable. [ABSTRACT FROM AUTHOR]

Although it is widely recognized that Foreign Direct Investment (FDI) inflows have a dominant effect on economic growth of host countries, the determinants of FDI inflows are still unclear. Especially, about the effect of exchange rate on FDI inflow, the results reached by scholars vary across countries or regions. It is of great practical and theoretical significance to explore the influencing effects of exchange rate on FDI inflow and identify the mechanisms that underlie them in close association with regional economic characters so as to help local government implement targeted government policies to achieve sustainable FDI inflow and sustainable economic growth. For this purpose, the influencing effects and the influencing mechanisms of the exchange rate on FDI inflows are investigated for Zhejiang province, China, over 1985–2019 by employing the co-integration tests, vector error correction models, Granger causality tests, and impulse response tests. Empirical results indicate that there are long-term stable and unidirectional causal relationship between the exchange rate and FDI inflow. Continuous appreciation of RMB against USD discourages FDI inflow. The mechanism which underlies the long-term relationship is the wealth effect, rather than the cost effect or the demand effect. By contrast, in the short run, neither the exchange rate nor the three influencing mechanism has a significant impact on FDI inflow. These results suggest policy recommendations for improving FDI by accumulating human capital and improving infrastructure. These findings are also applicable for other countries or regions with similar economic characters. [ABSTRACT FROM AUTHOR]

Since the beginning of 2018, Sino-US trade frictions have been escalating to the fields of science and technology, finance, and geography. Especially in the financial field, the United States has forcibly identified China as a "currency manipulator." In order to analyze the impact of Sino-US trade on the RMB exchange rate, based on the Sino-US import and export trade data under the quarterly HS classification from 2003 to 2019 and the RMB real effective exchange rate, this article carries out the traditional time series test, seasonal unit root test, and cointegration test and further constructs the seasonal error correction model to explore the long-term and short-term dynamic impact of Sino-US import and export trade structure on RMB real effective exchange rate. The results shows that the upgrading and optimization of the overall trade structure between China and the United States will increase the appreciation pressure of RMB real effective exchange rate. There are seasonal and long-term trends between RMB real effective exchange rate and different types of import and export trade structures between China and the United States. Therefore, this article not only strongly refutes the "theory of RMB appreciation" and puts forward policy suggestions to effectively deal with the negative impact of Sino-US trade friction but also provides a research framework for global trade, especially the decoupling of trade structure and exchange rate between developing and developed countries. [ABSTRACT FROM AUTHOR]

The world has witnessed the adverse impact of the COVID-19 pandemic. Accordingly, it is expected that information transmission between equities and digital assets has been altered due to the hostile impact of the pandemic outbreak on financial markets. As a result, the ensuing perverse risk among markets is presumed to rise during severe uncertainties occasioned by the COVID-19 pandemic. The impetus of this study is to examine the degree of asymmetry and nonlinear directional causality between global equities and cryptocurrencies in the frequency domain. Hence, we employ both the variational mode decomposition (VMD) and the Rényi effective transfer entropy techniques. Analyses of the study are presented for three sample periods; these are the full sample period, the pre-COVID-19 period, and the COVID-19 pandemic period. We gauge a mixture of asymmetric and nonlinear bidirectional and unidirectional causality between global equities and cryptocurrencies for the sample periods. However, the COVID-19 pandemic period appears to be driving the estimates for the full sample period, which indicates a negative flow. Thus, the direction and significance of the information flow between the markets for the full sample correspond to the one observed during the COVID-19 pandemic period. We, consequently, establish a significant directional, dynamical, and scale-dependent information flow between global equities and cryptocurrencies. Notwithstanding, throughout the study samples, we mainly find a negative significant information flow from global equities to cryptocurrencies. We detect that most cryptocurrencies exhibit similar behaviour of information flow to global equities for each of the sample periods. The outcome provides pertinent signals to investors with diverse investment horizons who would want to diversify, hedge, or employ cryptocurrencies as a safe haven for global equities during uncertainties, specifically the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Accurately forecasting China's total electricity consumption is of great significance for the government in formulating sustainable economic development policies, especially, China as the largest total electricity consumption country in the world. The calculation method of the background value of the GM(1, 1) model is an important factor of unstable model performance. In this paper, an extrapolation method with variable weights was used for calculating the background value to eliminate the influence of the extreme values on the performance of the GM(1, 1) model, and the novel extrapolation-based grey prediction model called NEGM(1, 1) was proposed and optimized. The NEGM(1, 1) model was then used to simulate the total electricity consumption in China and found to outperform other grey models. Finally, the total electricity consumption of China from 2018 to 2025 was forecasted. The results show that China's total electricity consumption will be expected to increase slightly, but the total is still very large. For this, some corresponding recommendations to ensure the effective supply of electricity in China are suggested. [ABSTRACT FROM AUTHOR]

The accuracy of time series forecasting is more important and can assist organizations to take up-to-date decisions for better planning and management. Several classical econometrics and computational approaches show promising results for the ordinary time series forecasting tasks, but they are not satisfactory in crude oil price forecasting. Ensemble empirical mode decomposition (EEMD) not only resolves the problem of nonlinearity and nonstationarity of time series prediction but also creates some problems (i.e., mood mixing and splitting). In this study, we proposed a new hybrid method that combines the median ensemble empirical mode decomposition and group method of data handling (MEEMD-GMDH) to reduce mood splitting problems and forecast crude oil price. MEEMD is achieved by replacing the mean operator with the median operator during the EEMD process. For testing and validation purposes of the different models, the two-seat stamp benchmarked crude oil price data are used (i.e., Brent and West Texas Intermediate (WTI)). To check the proposed model performance, different evaluation measures are used including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Diebold-Mariano (DM) test. All the forecasting accuracy measures confirmed that our proposed model performs well in crude oil prices forecasting as compared to other hybrid models. [ABSTRACT FROM AUTHOR]

Consistent estimation techniques need to be implemented to obtain robust empirical outcomes which help policymakers formulating public policies. Therefore, we implement the least squares (LS) and the high breakdown robust least trimmed squares (LTS) regression techniques, while using econometric regression model based on a growth equation for the two countries, namely, India and Pakistan. We used secondary annual time series data which covers a long period of 41 years. The adequacy of the time series econometric model was checked through cointegration analysis and found that there is no spurious regression. Classical and robust procedures were employed for the estimation of the parameters. The empirical results reveal that the overall fit of the model improves in case of LTS technique, while the significance of the predictors changes significantly in cases of both countries due to the removal of outliers from the data. Thus, empirical findings exhibit that the results, obtained through LTS, are better than LS techniques. [ABSTRACT FROM AUTHOR]

The Bitcoin exchange rate (BER) is influenced by many variables such as human speculation and policies and, thus, is dependent on the financial system. The fluctuation of BER submitted has been extensively investigated. However, the correlation analysis of the short- and long-term effects by indicators of online sentiment is unexplored. Therefore, this study establishes a VAR model for BER which provides a framework to the Google search volume index (SVI), the investor fear gauge (VIX), and the S&P500 Index. The findings of the analysis suggest that BER and Google SVI have a Granger causality feedback relationship in both the short- and long-term co-integration equilibrium, and the VIX is significantly related to BER in the long-term co-integration. [ABSTRACT FROM AUTHOR]

Flight delay is one of the most challenging threats to operation of air transportation network system. Complex network was introduced into research studies on flight delays due to its low complexity, high flexibility in model building, and accurate explanation about real world. We surveyed recent progress about flight delay which makes extensive use of complex network theory in this paper. We scanned analyses on static network and temporal evolution, together with identification about topologically important nodes/edges. And, we made a clarification about relations among robustness, vulnerability, and resilience in air transportation networks. Then, we investigated studies on causal relations, propagation modellings, and best spreaders identifications in flight delay. Ultimately, future improvements are summarized in fourfold. (1) Under Complex Network, flight operation relevant subsystems or sublayers are discarded by the majority of available network models. Hierarchical modelling approaches may be able to improve this and provide more capable network models for flight delay. (2) Traffic information is the key to narrow the gap between topology and functionality in current situations. Flight schedule and flight plan could be employed to detect flight delay causalities and model flight delay propagations more accurately. Real flight data may be utilized to validate and revise the detection and prediction models. (3) It is of great importance to explore how to predict flight delay propagations and identify best spreaders at a low cost of calculation complexity. This may be achieved by analyzing flight delay in frequency domain instead of time domain. (4) Summation of most critical nodes/edges may not be the most crucial group to network resilience or flight delay propagations. Effective algorithm for most influential sequence is to be developed. [ABSTRACT FROM AUTHOR]

For getting clear images and overcoming shaking caused by various disturbances, real-time compensation of pointing errors will improve the overall stability performance of photoelectric detection by unmanned aerial vehicle. However, the compensation will be greatly deteriorated by error-causing sources, and the error correction process is of great importance. In this research, the problem of stability precision error correction is comprehensively studied. First, by modeling overall kinematics, error-causing sources, and error compensation, the error correction process is mathematically modeled and simulated. Then, by using simulation data regression, error correction models including the global function model and parametric model are established. The models are validated by carrying out both simulations and validation experiments. At last, the performances of the error correction models are compared and analyzed, which concerns the factors of parameter identification, model simplicity, and final improvement effect. Results show that the final stability precision can be greatly improved over 20%, and the parametric model outperforms the global function model comprehensively. It can be concluded that, either in simulation environment or real application scenarios, the obtained models and related analysis results are effective in improving the system stability performance. [ABSTRACT FROM AUTHOR]

This paper employs the threshold cointegration methodology to assess the long- and short-run dynamics of asymmetric adjustment between economic policy uncertainty (EPU) of China-India, China-Japan, China-Korea, India-Japan, India-Korea, and Japan-Korea pairs using monthly EPU data ranging from January 1997 to April 2020. The relationship between the EPU pairs is examined in terms of Engle-Granger and threshold cointegrations. The findings provide evidence of long-run threshold cointegration and that the adjustments towards the long-run equilibrium position are asymmetric in the short run for the China-India and India-Japan EPU pairs in M-TAR specification with nonzero threshold values. Also, the results suggest a unidirectional causal relationship between China-India, China-Japan, and India-Korea EPU pairs in the long and short run using the spectral frequency domain causality approach. However, a bidirectional causal relationship between China-Korea, India-Japan, and Japan-Korea pairs exists in the long and short run. Therefore, the findings provide some clues to economic policymakers within the Asian subregion for possible policy uncertainty synergies and spillovers among the Asian countries. [ABSTRACT FROM AUTHOR]

We explore the relationship between unemployment and inflation in the United States (1949-2019) through both Bayesian and spectral lenses. We employ Bayesian vector autoregression ("BVAR") to expose empirical interrelationships between unemployment, inflation, and interest rates. Generally, we do find short-run behavior consistent with the Phillips curve, though it tends to break down over the longer term. Emphasis is also placed on Phelps' and Friedman's NAIRU theory using both a simplistic functional form and BVAR. We find weak evidence supporting the NAIRU theory from the simplistic model, but stronger evidence using BVAR. A wavelet analysis reveals that the short-run NAIRU theory and Phillips curve relationships may be time-dependent, while the long-run relationships are essentially vertical, suggesting instead that each relationship is primarily observed over the medium-term (2-10 years), though the economically significant medium-term region has narrowed in recent decades to roughly 4-7 years. We pay homage to Phillips' original work, using his functional form to compare potential differences in labor bargaining power attributable to labor scarcity, partitioned by skill level (as defined by educational attainment). We find evidence that the wage Phillips curve is more stable for individuals with higher skill and that higher skilled labor may enjoy a lower natural rate of unemployment. [ABSTRACT FROM AUTHOR]

The modern logistics industry has opened new strategic perspectives in establishing its interrelation with economic growth. In recent years, understanding such an overlap has become a policy issue considering ever-increasing factors and their influence on this relation. Most existing studies have explored this interaction from a general perspective, or for developed countries. This paper explores time-series analysis of the dynamic variables and their inter-related influence in both the short and long run on the relationship between modern logistic industry and economic growth—a more specific perspective, particularly for developing countries. Accordingly, we exemplify our analysis by employing the vector autoregression (VAR) model to the most updated time series data of investment in the logistics industry and the economic growth of Pakistan from 1990 to 2018. The empirical findings endorse the previous studies' outcomes and recognize the importance of sustainable economic development concerning continuously improving the logistics industry. However, a unidirectional relation is observed that economic growth leads to developing the logistics industry—economic growth exerts a significant demand-pull effect on Pakistan's logistics. It implies that logistic industrial development is comparatively quicker in the geographical areas where economic growth is higher than those areas where economic growth is low. To conclude this study's findings, logistics industry reforms should prioritize the selected geographical areas in improving the economy that would lead to the modern logistics industry's development. As the model adopts Pakistan's context, the overall statistical analysis can be generalized to other developing economies. These results would be of particular interest to strategy makers working in developing countries and help them design and develop modern transportation and logistics, coupled with interlinked technological factors, which would attract investment in the logistics industry for sustainable economic development. [ABSTRACT FROM AUTHOR]

Studies on the role of renewable energy consumption and other environmental factors in carbon emission have got considerable attention recently, and they are predicted to get exaggerated in the coming decades. Energy usage increases economic growth and development of a country and backs to global warming and carbon emission which affect the local environment. For the prosperity of a country, it is felt crucial to measure the unavoidable impacts which effect environmental quality. Consequently, the current study investigates the interrelationship of renewable energy consumption, carbon dioxide emission, foreign direct investment, and economic growth in 190 countries of the world for the period of 1980 to 2018. By employing both static and dynamic models, the findings indicate that carbon emission, renewable energy consumption, foreign direct investment, and economic growth affect each other significantly whereas renewable energy consumption has been found beneficial for environmental quality; however, it decreases the inflow of FDI. RE has a decreasing impact, while FDI and carbon emission promote economic growth. The study suggests the promotion of renewable energy resources and policies related to FDI to promote the quality of the environment and achieve economic growth as well. [ABSTRACT FROM AUTHOR]

The interferometer is a widely used direction-finding system with high precision. When there are comprehensive disturbances in the direction-finding system, some scholars have proposed corresponding correction algorithms, but most of them require hypothesis based on the geometric position of the array. The method of using machine learning that has attracted much attention recently is data driven, which can be independent of these assumptions. We propose a direction-finding method for the interferometer by using multioutput least squares support vector regression (MLSSVR) model. The application of this method includes the following: the construction of MLSSVR model training data, training and construction of the MLSSVR model, and the estimation of direction of arrival. Finally, the method is verified through numerical simulation. When there are comprehensive deviations in the system, the direction-finding accuracy can be effectively improved. [ABSTRACT FROM AUTHOR]

As is known, natural gas consumption has been acted as an extremely important role in energy market of China, and this paper is to present a novel grey model which is based on the optimized nonhomogeneous grey model (ONGM (1,1)) in order to accurately predict natural gas consumption. This study begins with proving that prediction results are independent of the first entry of original series using the product theory of determinant; on this basis, it is a reliable approach by inserting an arbitrary number in front of the first entry of original series to extract messages, which has been proved that it is an appreciable approach to increase prediction accuracy of the traditional grey model in the earlier literature. An empirical example often appeared in testing for prediction accuracy of the grey model is utilized to demonstrate the effectiveness of the proposed model; the numerical results indicate that the proposed model has a better prediction performance than other commonly used grey models. Finally, the proposed model is applied to predict China's natural gas consumption from 2019 to 2023 in order to provide some valuable information for energy sectors and related enterprises. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the effects of demographic variables on tea consumption in China. A total of 12,745 samples collected from the China Health and Nutrition Survey in 2011 and a double hurdle model were used to analyze the effects of demographic variables on the extent of tea participation and consumption quantity for men and women. The results of this study indicate that the effects of demographic variables differ between genders in terms of tea participation decisions and consumption quantity decisions. For men, education, family size, region, and employment status were found to exert different effects on participation and consumption quantity decisions; for women, age, education, region, employment status, having elderly individuals in the home, and urbanization exerted different effects on their tea participation and consumption decisions. The most obvious difference between variables affecting men and women pertained to age, family size, and having elderly individuals in the home. The perspectives of each gender, coupled with the double hurdle model used in this study, offer important insights. [ABSTRACT FROM AUTHOR]

In the midst of macro-economic uncertainties, accurate long-term exchange rate forecasting is crucial for decision-making and planning. To measure the uncertainty associated with exchange rate and obtaining additional information of future exchange rate, a hybrid model based on quantile regression forest and Gaussian kernel (GQRF) is constructed. Quarterly dataset of KSh/USD exchange rate and macro-economic variables from 2007 to 2016 are used. The forecast horizon spans from 2013 to 2016. With a prediction interval coverage probability and prediction interval average width of 95% and 29.6493%, the constructed model has a very high coverage probability. The method of determining the probabilistic forecasts is very significant to achieve forecasts with correct coverage. The probability density forecasting model for the exchange rate gave significant information–the probability distribution of the forecasted results. In this way, uncertainties around the forecast can be evaluated because the complete exchange rate distribution are forecasted. GQRF is efficient as it can uphold the uncertainty about the variance linked to each point, which is important for exchange rate forecasting. Using the constructed model, the probabilities of exceedance such as the probability of future exchange rate exceeding the average exchange rate for the year can be computed. This paper also adds to the scarce literature of exchange rate probability density forecasting using machine learning techniques. [ABSTRACT FROM AUTHOR]

The Multivariate Geographically Weighted Regression (MGWR) model is a development of the Geographically Weighted Regression (GWR) model that takes into account spatial heterogeneity and autocorrelation error factors that are localized at each observation location. The MGWR model is assumed to be an error vector ε that distributed as a multivariate normally with zero vector mean and variance-covariance matrix Σ at each location u i , v i , which Σ is sized q x q for samples at the i -location. In this study, the estimated error variance-covariance parameters is obtained from the MGWR model using Maximum Likelihood Estimation (MLE) and Weighted Least Square (WLS) methods. The selection of the WLS method is based on the weighting function measured from the standard deviation of the distance vector between one observation location and another observation location. This test uses a statistical inference procedure by reducing the MGWR model equation so that the estimated error variance-covariance parameters meet the characteristics of unbiased. This study also provides researchers with an understanding of statistical inference procedures. [ABSTRACT FROM AUTHOR]

The accurate differentiation of the subtypes of benign paroxysmal positional vertigo (BPPV) can significantly improve the efficacy of repositioning maneuver in its treatment and thus reduce unnecessary clinical tests and inappropriate medications. In this study, attempts have been made towards developing approaches of causality modeling and diagnostic reasoning about the uncertainties that can arise from medical information. A dynamic uncertain causality graph-based differential diagnosis model for BPPV including 354 variables and 885 causality arcs is constructed. New algorithms are also proposed for differential diagnosis through logical and probabilistic inference, with an emphasis on solving the problems of intricate and confounding disease factors, incomplete clinical observations, and insufficient sample data. This study further uses vertigo cases to test the performance of the proposed method in clinical practice. The results point to high accuracy, a satisfactory discriminatory ability for BPPV, and favorable robustness regarding incomplete medical information. The underlying pathological mechanisms and causality semantics are verified using compact graphical representation and reasoning process, which enhance the interpretability of the diagnosis conclusions. [ABSTRACT FROM AUTHOR]

Along with the improvement of Chinese people's living standard, the proportion of residential energy consumption in total energy consumption is rapidly increasing in China year by year. Accurately forecasting the residential energy consumption is conducive to making energy programming and supply plan for the administrative departments or energy companies. By improving the grey action quantity of traditional grey model with an exponential time term, a novel power-driven grey model is proposed to forecast energy consumption as reference data for decision makers. e nonlinear parameter of power-driven grey action quantity is a crucial factor to influence the prediction precision. To promote the prediction accuracy of the power-driven grey model, whale optimization algorithm is adopted to seek for the optimal value of the nonlinear parameter. Two validations on real-world datasets are conducted, and the results indicate that the power-driven grey model has significant advantages on the aspect of prediction performance compared with the other seven classical grey prediction methods. Finally, the power-driven grey model is applied in forecasting the total residential energy and the thermal energy consumption of China. [ABSTRACT FROM AUTHOR]

The mesh model and mesh stiffness representation are the two main factors affecting the calculation method and the results of the dynamic mesh force. Comparative studies considering the two factors are performed to explore appropriate approaches to estimate the dynamic meshing load on each contacting tooth flank of spiral bevel gears. First, a tooth pair mesh model is proposed to better describe the mesh characteristics of individual tooth pairs in contact. The mesh parameters including the mesh vector, transmission error, and mesh stiffness are compared with those of the extensively applied single-point mesh model of a gear pair. Dynamic results from the proposed model indicate that it can reveal a more realistic and pronounced dynamic behavior of each engaged tooth pair. Second, dynamic mesh force calculations from three different approaches are compared to further investigate the effect of mesh stiffness representations. One method uses the mesh stiffness estimated by the commonly used average slope approach, the second method applies the mesh stiffness evaluated by the local slope approach, and the third approach utilizes a quasistatically defined interpolation function indexed by mesh deflection and mesh position. [ABSTRACT FROM AUTHOR]

The article titled "Analysis of Dynamic Relationship between Energy Consumption and Economic Growth Based on PVAR Model" has been retracted by the publisher, Hindawi, due to evidence of systematic manipulation of the publication and peer-review process. The reliability and integrity of the article cannot be vouched for. Wiley and Hindawi have implemented additional measures to ensure research integrity. The corresponding author has been given the opportunity to respond to the retraction. [Extracted from the article]

This paper investigates the impact of foreign direct investment (FDI), trade openness, domestic demand, and exchange rate on the export performance of Bangladesh over the period of 1980-2009 using the vector error correction (VEC) model under the time series framework. The stationarity of the variables is checked both at the intercept and intercept plus trend regression forms under the ADF and PP stationarity tests. The Johansen-Juselius procedure is applied to test the cointegration relationship between variables followed by the VEC regression model. The empirical results trace a long-run equilibrium relationship in the variables. FDI is found to be an important factor in explaining the changes in exports both in the short run and long-run. However, the study does not trace any significant causal relationship for the cases of trade openness, domestic demand, and exchange rate. The study concludes that Bangladesh should formulate FDI-led polices to enhance its exports. [ABSTRACT FROM AUTHOR]