Search Results (56)

The two groups of EU economies, the euro area and the non-euro area, are statistically analyzed taking into account the fulfillment of the euro area financial criteria and economic performance over the period 2000–2023. Compliance with financial criteria, economic performance, and their significant influencing factors are presented comparatively for the two groups of countries. The long-run equilibrium between economic growth and its factors is identified by econometric approaches with the error correction model (ECM) and autoregressive distributed lag (ARDL) models for the two data panels. In the short term, economic shocks are taken into account to compare their different influences on economic growth within the two groups of countries. The GMM system is used to model economic convergence at the EU level over the period under review. Comparisons between GDP growth and its theoretical values from econometric models have led to interesting conclusions regarding the existence and characteristics of economic convergence at the group and EU level. EU countries outside the euro area have higher economic growth rates than euro area economies over the period 2000–2023. In the long run, investment brings a higher increase in economic development in EU countries outside the euro area than in euro area countries. Economic shocks have been felt more deeply on economic growth in the euro area than in the non-euro area. The speed of adjustment towards long-run equilibrium in econometric models is slower for non-euro area economies than in the euro area over a one-year period. At the level of the European Monetary Union, change policies have a faster impact on economic development and a faster speed of adjustment towards equilibrium. Full article

The global movement towards a cashless society has prompted the payment of tax obligations through digital platforms and sources. In this international race to ensure that transaction payments are not hindered by the lack of physical cash, Nigeria is also making progress. Therefore, the focus of this study is to assess the implications of digital payment systems in enhancing the effectiveness of tax revenue collection in Nigeria. The analysis spans from the first quarter of 2009 to the fourth quarter of 2023, utilizing the Autoregressive Distributed Lag and Error Correction Model. The research uses the most active digital payment systems that have been in operation during the study period. These electronic payment types include digital cheques (CHQs), Automated Teller Machines (ATMs), Point-of-Sales (POSs), Mobile payment (MPY), and Web-based payment (WPY). These are the predictor variables, while the tax revenue collection (TXC) during this period is the dependent variable. The control variables include information and telecommunication technology penetration rate (ICTPR), inflation, and gross domestic product. The outcomes of this study reveal that, over the long term, a percentage change in CHQs, ATMs, MPY, and ICTPR is linked to a decline of 8.1%, 12.5%, 6.7%, and 22.4% in TXC, respectively. In contrast, WPY indicates a 7.2% positive increase in TXC while inflation exerts a positive increase of 46.7%. The Error Correction Model (ECM) suggests that the deviations from the long-term equilibrium in earlier years are being corrected at a rate of 3.9% in the current year. In the short term, it is noted that digital payment systems do not influence TXC. On the other hand, GDP maintains a significant negative influence on TXC, in both the long- and short-term. Given these results, the study recommends the establishment of a robust information and communication technology (ICT) infrastructure to enhance effective tax collection, even from rural areas and the informal sector. It is also important for the government to develop strategies that will bring the informal sector into the tax net. Full article

This paper examines the dynamic relationship between economic indicators, law enforcement mechanisms, and property-related crimes in Mongolia using a time-series econometric approach. Relying on the theoretical frameworks of Becker’s economic model of crime and Cantor and Land’s motivation–opportunity hypothesis, the study explores the effects of unemployment, detection probability, and incarceration rates on four crime categories: total crime, theft, robbery, and fraud. An error correction model (ECM) is employed to capture both short-run fluctuations and long-run equilibrium relationships over the period 1992–2022. The empirical findings reveal that detection rates exert a statistically significant deterrent effect on robbery in the short term, while incarceration rates are effective in reducing theft. Unemployment shows a positive and significant long-run effect on theft prior to 2009 but weakens thereafter due to methodological changes in labor statistics. Fraud demonstrates a distinct response pattern, exhibiting negative associations with both incarceration and unemployment, and showing no sensitivity to detection probability. Diagnostic tests support the model’s robustness, with heteroskedasticity in the theft model addressed using robust standard errors. This study contributes to the literature by providing the first country-specific empirical evidence on crime determinants in Mongolia. It highlights the heterogeneous impact of economic and institutional factors on different crime types in a transition economy. The findings underscore the need for integrated policy responses that combine improvements in law enforcement with inclusive economic and social development strategies. Full article

As the debt stock level of Ghana continues to rise, partly due to the negative impact of COVID-19, a number of new taxes have been introduced in the 2021 budget statement alongside an upward adjustment of electricity tariff. State borrowing may significantly influence electricity tariff, as power generation and distribution are primarily undertaken by state-owned companies whose borrowing constitutes a substantial portion of the country’s overall debt. Hence, this paper assesses the impact of state debt on electricity tariff in Ghana post COVID-19. The autoregressive distributed lag (ARDL) model and error correction model (ECM) are employed to test for the Granger causality between state debt and electricity tariff. Other variables such as inflation rates, exchange rates, and net energy imports that have the propensity to influence electricity tariff are also examined. The results reveal that state debt has both short-term and long-term impacts on electricity tariff. Additionally, inflation rates, exchange rates, and net energy imports only have long-term impacts on electricity tariff. Meanwhile, exchange rates have short-term effects on state debt. The findings imply that effective debt management policies should be implemented by the government to reduce borrowing, particularly when such borrowing is not invested into projects that can repay the debt at maturity. This study demonstrates that all the accumulated debt prior to and during the COVID-19 era is causing a significant increase in Ghana’s electricity tariff. This provides an empirical clue as to what the situation is likely to be in other developing countries. Full article

In this study, we propose a model to forecast container throughput for the Singapore port, one of the busiest ports globally. Accurate forecasting of container throughput is critical for efficient port operations, strategic planning, and maintaining a competitive advantage. Using monthly container throughput data of the Singapore port from 2010 to 2021, we develop a Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model. For the exogenous variables included in the SARIMAX model, we consider the West Texas Intermediate (WTI) crude oil price and China’s export volume, alongside the impact of the COVID-19 pandemic measured through global confirmed cases. The predictive performance of the SARIMAX model was evaluated against a diverse set of benchmark methods, including the Holt–Winters method, linear regression, LASSO regression, Ridge regression, ECM (Error Correction Mechanism), Support Vector Regressor (SVR), Random Forest, XGBoost, LightGBM, Long Short-Term Memory (LSTM) networks, and Prophet. This comparative analysis was conducted by forecasting container throughput for the year 2022. Results indicated that the SARIMAX model, particularly when incorporating WTI prices and China’s export volume, outperformed other models in terms of forecasting accuracy, such as Mean Absolute Percentage Error (MAPE). Full article

This paper proposes a novel physics-based equivalent circuit model of the lithium-ion battery for electric vehicle applications that has comprehensive electrochemical significance and an acceptable level of complexity. Initially, the physics-based extended single particle (ESP) model is improved by adding a correction term to mitigate its voltage bias. Then, the equivalent circuit model based on the improved extended single particle (ECMIESP) model is derived. In this model, the surface state of charge (SOC) of solid particles is approximated using a capacity and multi first-order resistance-capacity equivalent circuits with only two lumped parameters. The overpotential of electrolyte diffusion is approximated using a first-order resistance-capacitance equivalent circuit. The electrochemical reaction overpotential is characterized by a nonlinear resistance. The voltage accuracies of ECMIESP and conventional 2RC equivalent circuit model (ECM2RC) are compared across the entire SOC range under various load profiles. The results demonstrate that the ECMIESP model outperforms ECM2RC model, particularly at low SOC or when the electrochemical reaction overpotential exceeds 50 mV. For instance, the ECMIESP model shows an 820.4 mV reduction in voltage error compared to the ECM2RC model at the endpoint during a 2C constant current discharge test. Lastly, the ECMIESP model was used for SOC estimation with extended Kalman filter, resulting in significantly improved accuracy compared to the conventional ECM2RC model. Therefore, the ECMIESP model has great potential for real-time applications in enhancing voltage and SOC estimation precision. Full article

This study is the first to investigate the asymmetric effects of oil price volatility on stock returns for the Ho Chi Minh Stock Exchange (HOSE). We utilized weekly series of VN30-Index, WTI crude oil prices, geopolitical risks (GPR) index, and gold prices spanning from 6 February 2012 to 31 December 2023 as data sources. Using a nonlinear autoregressive distributed lag (NARDL) bounds testing approach, we found that, in the shortterm, oil price volatility has negative asymmetric effects on market returns. Specifically, in the shortterm, a 1 percent increase in oil price volatility immediately leads to a 2.6868 percent decrease in the market returns, while a similar magnitude decrease in oil price volatility is associated with a 6.3180 percent increase in the market returns. In addition, the results obtained from the NARDL model indicated that, in the longterm, the negative and positive changes of oil price volatility have significantly negative effects on the market returns. Finally, the findings derived from the error correction model (ECM) show that a 98.21 percent deviation from the equilibrium level in the previous week is converged and corrected back to the long-term equilibrium in the current week. Full article

The labor force is a crucial factor in conducting economic activities, especially in labor-surplus countries like Pakistan. In this study, we explore the impact of labor force participation (LF) on economic growth (EG), with an emphasis on how this impact depends on the levels of health and education expenditures. We analyze time series data from Pakistan spanning from 1980 to 2022, using ARDL (Autoregressive Distributed Lag), ECM (Error Correction Model) and Granger causality techniques for empirical analysis. The ARDL results indicate that LF significantly boosts EG, both in the short and long run. Furthermore, the estimations reveal that better facilities for health and education strengthen the positive effects of LF on EG. This suggests a complementary relationship between health, education, and LF in driving EG. Moreover, our findings highlight the temporal significance of health and education: Health plays a more crucial role in the short run, while education’s impact is more substantial in the long run. Furthermore, the Granger causality results indicate that LF, health, and education significantly contribute to EG. It is advisable for the government to prioritize investments in the health and education sectors. This approach can empower individuals to actively and effectively participate in economic activities, eventually contributing to the overall economic output of the nation. Full article

The growing interest in low-impact mobility technologies has elevated the significance of fuel cell electric vehicles (FCEVs) in the automotive sector. Given the complexity of the resulting powertrain, the need for an effective energy management strategy (EMS) becomes essential to optimize efficiency and energy consumption in vehicles with diverse energy sources. Model-based control is the main approach to address the EMS in electrified vehicles. In particular, fuel cell power is commonly represented through a 1D look-up table using the current demand as input to simplify the implementation in a vehicle control unit. Uncertainties that may be implemented in maps due to simplifying hypotheses, dynamics, ageing, etc., can be propagated to powertrain control, motivating the adoption of adaptive look-up tables for FC modelling. In this study, an extended Kalman filter (EKF) is proposed to adapt the look-up table to actual FC behaviour by measuring its power and gradually correcting calibration errors, drift, and ageing. Subsequently, a standard equivalent consumption minimization strategy (ECMS) is employed to control the FCEV. The fuel cell model is calibrated with experimental data from an FCEV. The results demonstrate that the adaptive strategy outperforms the base calibration. Following an extensive simulation campaign, an improvement of 1.1% in fuel consumption was observed. Remarkably, after just one hour of operation, there was a notable 85% reduction in fuel cell power estimation error, even when the EMS was initially fed a biased look-up table. Full article

The carbon–financial nexus helps firms evaluate susceptibility to carbon risk more effectively. This is the first research article to model the short- and long-run co-integrating association between European financial markets, the CBOE oil price volatility index (OVZ) and the European carbon emission trading system (EU-ETS) by using the daily returns from 1 October 2013 to 1 October 2023. We utilize co-integration test followed by the ARDL framework with an error correction mechanism (ECM). Moreover, we utilize the DCC-GARCH-t copula framework to estimate the hedge ratio and to select an optimal portfolio weight for carbon risk hedging. Overall, the findings suggested that EU-ETS (OVZ) has a consistent positive (negative) short-term influence on all the equity returns of Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Netherlands, Spain and the stock indices of the whole Eurozone. However, in the long term, EU-ETS has a positive (negative) effect on the stock returns of France and the Eurozone (Belgium and Spain). Belgian and Spanish companies could implement long-term carbon reduction policies. Belgian and Spanish firms should focus on the utilization of green energy resources and the internalization of carbon emission-free mechanical processes as this may offer a safeguard against the additional pressure arising from escalating carbon prices. Finally, an optimal portfolio weight selection strategy based upon the DCC-GARCH-t copula approach aims for higher hedging effectiveness (HE) than the hedge ratio strategy when adopting short-term positions in Italian and Danish equity markets to reduce the risk of long-term EU-ETS volatility. Full article

Foreign direct investment (FDI) is a key driver of economic development of both developed and developing countries. Understanding and having insights into the factors that motivate increased FDI arevery important for both academics and policy makers. A key factor that multinationals incorporate in their decisions on FDI is geopolitical risk (GPR). Therefore, this study is devotedto investigating the short-term and long-term effects of GPR on FDI in Vietnam. Data used in this study are the yearly geopolitical risk index, FDI, and other control variables covering the period from 1986 to 2021. Using the autoregressive distributed lag (ARDL) bounds testing approach, the empirical results confirm that geopolitical risk (GPR) has a significantly negative effect on FDI in Vietnam in the longterm. Specifically, in the longterm, 1 percent increase in the GPR index is associated with 5.7983 percent decrease in Vietnam’s FDI. In addition, the results derived from the ARDL model indicate that in the shortterm, GPR has a significantly positive effect on the FDI for the one-year lag, meaning that an increase in the GPR index leads to an increase in FDI. Moreover, the results derived from the error correction model (ECM) indicate that 42.89% of the disequilibria from the previous year are converged and corrected back to the long-run equilibrium in the current year. Based on the findings, some policy implications are drawn for policymakers to mitigate the negative effects of GPR on FDI. Full article

This study explored the complex interplay and potential risk of financial contagion across major financial indices, focusing on the Bucharest Exchange Trading Investment Funds Index (BET-FI), along with global indices like the S&P 500, Nasdaq Composite (IXIC), and Dow Jones Industrial Average (DJIA). Our analysis covered an extensive period from 2012 to 2023, with a particular emphasis on Romania’s financial market. We employed Autoregressive Distributed Lag (ARDL) modeling to examine the interrelations among these indices, treating the BET-FI index as our primary variable. Our research also integrated Exponential Curve Fitting (EXCF) and Generalized Supremum Augmented Dickey–Fuller (GSADF) models to identify and scrutinize potential price bubbles in these indices. We analyzed moments of high volatility and deviations from typical market trends, influenced by diverse factors like government policies, presidential elections, tech sector performance, the COVID-19 pandemic, and geopolitical tensions, specifically the Russia–Ukraine conflict. The ARDL model revealed a stable long-term relationship among the variables, indicating their interconnectedness. Our study also highlights the significance of short-term market shifts leading to long-term equilibrium, as shown in the Error Correction Model (ECM). This suggests the existence of contagion effects, where small, short-term incidents can trigger long-term, domino-like impacts on the financial markets. Furthermore, our variance decomposition examined the evolving contributions of different factors over time, shedding light on their changing interactions and impact. The Cholesky factors demonstrated the interdependence between indices, essential for understanding financial contagion effects. Our research thus uncovered the nuanced dynamics of financial contagion, offering insights into market variations, the effectiveness of our models, and strategies for detecting financial bubbles. This study contributes valuable knowledge to the academic field and offers practical insights for investors in turbulent financial environments. Full article

Deforestation is a central topic in the ongoing environmental degradation stemming from global economic expansion and population growth. This study delved into the effects of electricity production from renewable sources, GDP per capita, and urbanization on forest area growth in Finland during the over-three-decade research period, 1990–2022, using an Autoregressive Distributed Lag (ARDL) model. Both the ARDL bounds test and the Bayer–Hanck cointegration tests proved the existence of a long-term cointegrating relationship between the variables, and the constructed error correction model (ECM) evaluated short-term relationships. The results showed that: (i) forest area growth is positively connected with electricity production from renewable sources and urbanization; (ii) forest area growth is negatively connected with economic growth; (iii) in the short run, forest area growth is positively connected with all regressors. The utilized ARDL-ECM model, characterized by its robustness and appropriateness, validated the time-series dynamics. The obtained results were scrutinized, and their policy implications were thoroughly examined. Additionally, recommendations are provided to ensure the sustainability and success of forest conservation efforts. Full article

According to the National Financial Inclusion Strategy (NFIS), Bangladesh aims to achieve a 100% financial inclusion target by 2026 through mobile financing services (MFSs). However, despite several efforts, the financial inclusion score remained only 53% at the end of 2021, compared to 50% in 2017. A substantial proportion of this growth came through MFSs during the COVID-19 pandemic. This article investigates the short-run and long-run influence of COVID-19 movement restriction orders on MFSs. An autoregressive distributed lag model (ARDL) is applied to the monthly transaction data over the period of December 2016 to May 2022 of the three most popular MFSs. Movement restriction orders are associated with a significant increase in person-to-person transactions (P2P) and person-to-business transactions (P2B) in the long run, but the effect is positive and statistically insignificant for remittance transfer. Furthermore, using the volume of ATM transactions as a measure of financial inclusion, this study confirms the crucial role of movement restriction orders in intensifying the financial inclusion of Bangladesh through MFSs. The coefficients of error correction models (ECM) indicate that policymakers must act promptly to develop actionable strategies to maintain the short run momentum of the demand for MFSs to achieve the national target. Full article

This study compares how crude oil and ethanol price changes are passed through to the wholesale prices of a conventional fuel (E10), which contains 10% ethanol, and a high-blend ethanol fuel (E85), which contains 51% to 83% ethanol. Daily observations from October 2017 to June 2019 were obtained from a large market in the United States that provided wholesale fuel prices and ethanol prices. The Error Correction Model (ECM) was applied to each fuel specification using Seemingly Unrelated Regressions (SURs) in order to improve the efficiency of the estimates. Comparable to prior research, the long-run pass-through coefficient for E10 with respect to crude oil was 1.13. In contrast, the E85 long-run pass-through coefficient with respect to crude oil was 0.74. Estimates for the short-run analysis indicated asymmetry in the transmission of crude oil price changes to E10, with crude price increases passing through at greater rates compared to crude price decreases. Symmetry was found in the transmission of ethanol price changes to E85, indicating the same response to rising and falling ethanol costs. Despite the differences in ethanol requirements, the relative prices of crude oil and ethanol are still important for both fuels. Full article