Search Results (74)

Egypt has experienced a sharp rise in external debt over the past decade, increasing from USD 55.8 billion in 2015 to over USD 165.3 billion by 2023. Despite maintaining a debt-to-GDP ratio within internationally accepted thresholds (approximately 45% in 2023), the country faces mounting economic distress, including foreign exchange shortages, currency depreciation, and rising debt-servicing burdens. This study argues that Egypt’s crisis stems not from excessive borrowing but from ineffective debt management, particularly the misalignment between debt maturities and the economic returns of financed projects. Using annual data from 2010 to 2023—a period deliberately selected to capture Egypt’s post-2011 political and economic transition—the analysis applies a Vector Autoregression (VAR) model and Granger causality test to explore short-term interactions between short-term and long-term external debt, the exchange rate, and foreign reserves. While the small sample size limits long-term econometric inference, it provides meaningful insights into short-term debt dynamics and liquidity pressures characteristic of Egypt’s current economic phase. The results show that short-term debt exerts significant depreciative pressure on the currency, while long-term debt weakly undermines reserves when tied to non-revenue-generating projects. Policy recommendations emphasize improving debt maturity alignment, enhancing transparency, and linking debt servicing to productive investments. Full article

This study investigates the dynamic relationship between interest and noninterest income at Azer Turk Bank using quarterly data from 2016Q1–2024Q3. Unit root tests including Augmented Dickey–Fuller (ADF), Kwiatkowski–Phillips–Schmidt–Shin (KPSS), and Fourier–KPSS indicate that both variables are non-stationary in levels but become stationary after first differencing. The Hylleberg–Engle–Granger–Yoo (HEGY) test further shows that both series contain a unit root at the non-seasonal (0) frequency, while no unit roots are detected at the seasonal frequencies (π/2 and 3π/2). Johansen cointegration and the Fourier Autoregressive Distributed Lag (Fourier–ADL) framework confirm the existence of a stable long-run equilibrium. As a key methodological contribution, the study derives explicit Fourier-based Vector Error Correction Model (VECM) equations, enabling the modeling of cyclical deviations around nonlinear trends. Fourier Toda–Yamamoto and Breitung–Candelon frequency-domain causality tests reveal asymmetry: interest income consistently drives noninterest income in the short and medium run, whereas the reverse effect is weak. The results also confirm mean reversion, with deviations from equilibrium corrected within 5.9; 2.5 quarters. Overall, the findings highlight the limited diversification potential of noninterest income and the decisive role of lending in bank revenues, offering both methodological advances and practical guidance for macroprudential policy. Full article

The COVID-19 pandemic and the implementation of strict lockdown measures have significantly impacted various dimensions of the global economy. This study examines the impact of COVID-19 and lockdown stringency on exchange rate volatility in India using three core variables, i.e., COVID-19 cases, the lockdown stringency index, and exchange rate volatility. To achieve the above objectives, we have employed advanced econometric techniques, such as wavelet coherence and a hybrid non-parametric quantile causality framework, on the dataset spanning from 30 December 2020 to 24 January 2022. Robustness is assessed using Troster–Granger causality in quantiles and Breitung–Candelon Spectral Causality tests. The wavelet coherence analysis indicates that the initial outbreak of COVID-19 increased the exchange rate volatility, while the enforcement of stringent lockdowns in the later phases helped reduce this volatility. Similarly, the hybrid quantile causality results indicate that both COVID-19 cases and lockdown measures possess predictive power over exchange rate fluctuations. The robustness checks confirm these findings and establish a causal relationship between the pandemic, policy responses, and currency market behaviour. This study helps clarify the complex, nonlinear dynamics between pandemic-related variables and exchange rate volatility in emerging markets. Based on the aforementioned result, it is recommended that policymakers implement targeted lockdown strategies coupled with timely monetary interventions (such as foreign exchange reserve management or interest rate adjustments) to mitigate volatility and maintain currency stability during future pandemic-induced shocks. Full article

Concerns about ensuring a sustainable environment are growing, attracting major attention from policy professionals worldwide. Therefore, this study investigates the nonlinear impacts of fiscal consolidation on CO₂ emissions in 17 OECD countries from 1978 to 2020. To probe the short- and long-term connections across various quantiles of CO₂ emissions, we adopted panel QARDL frameworks. The Granger non-causality test was used to investigate the variables’ association with CO₂ emission. The study’s main findings confirm the overall beneficial effect of fiscal consolidation on carbon emissions. It reduces CO₂ emissions at almost all quantiles in the short run. By contrast, in the long run, the effect is positive at lower quantiles and turns negative at upper quantiles. Furthermore, a causality analysis identified a bidirectional causal relationship between fiscal consolidation and CO₂ emissions, confirming the existence of mutual influence. While Keynesian theory links fiscal consolidation to economic recession, our findings support the non-Keynesian view, showing that such policy can foster economic growth and thereby contribute to reducing CO₂ emissions in the short run. Thus, OECD countries are orienting public spending and carbon taxation toward environmentally friendly practices while ensuring environmental protection and deficit reduction. Nonetheless, the identified mixed effect in the long run highlights the need for sustained consolidation policies by enhancing expenditure efficiency and adopting targeted taxation measures to achieve lasting emission reductions and support the transition to cleaner energy, even when emissions are relatively low. Full article

The Southern African Customs Union (SACU), as a bloc, is compelled to commit to trade in environmentally friendly goods. This study investigated the short-run and long-run relationships between trade openness and environmental quality in the SACU. The Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) approach was applied to the data from 1985 to 2023. The results show that the estimated coefficients of trade openness positively and significantly contribute to carbon emissions in the short run and the long run. The results demonstrate that the gains-from-trade hypothesis does not hold in the SACU. Also, the results indicate that foreign direct investment inflow does not significantly contribute to CO₂ emissions; therefore, the pollution haven hypothesis does not hold. The Dumitrescu–Hurlin Granger non-causality test was employed, and the results show that there is bidirectional causality between CO₂ emissions and trade openness, CO₂ emissions and economic growth, and CO₂ emissions and population growth and no directional causality between foreign direct investment and CO₂ emissions. This study recommends that SACU countries should encourage the trade of eco-friendly goods, which is likely to lessen environmental consequences. Full article

The investment–saving relationship has been the subject of much debate. On the one hand, there is the conventional mainstream neoclassical school of thought that advocates for the idea that saving determines investment. On the other hand, heterodox economists (mainly in the post-Keynesian/structuralist tradition) posit an inverse relationship between these variables. This article empirically investigates the direction of causality in order to contribute to the existing literature on the topic. To this end, two Granger panel tests are applied to a dataset of 106 countries over the period from 1980 to 2023. The econometric techniques used are effective in accounting for both cross-sectional dependence and heterogeneity in the data. In summary, our findings align with the theoretical models that posit bidirectional causality as the most probable explanation of the mechanism driving investment and saving. More specifically, they are consistent with post-Keynesian (demand-led) assumptions describing an open economy operating below its maximum potential growth rate within a current account solvency constraint. Full article

This paper investigates the impact of global EPU and China’s EPU on China’s FDI inflows, examining whether financial development and political stability moderate these relationships. Using panel data from 212 countries spanning 2009 to 2022, we first establish causal direction through Granger causality tests, then employ instrumental variable estimation to address endogeneity concerns, while conducting heterogeneity analysis across development levels and Belt and Road Initiative participation. We find that both global and domestic EPU significantly reduce China’s FDI inflows, with a 1% increase in China’s EPU leading to a 0.083% decrease in FDI inflows. However, political stability and financial development serve as effective moderators, reducing EPU’s negative impact by up to 60% and 70%, respectively. The effects vary substantially across investor countries: non-developed countries show ten times stronger sensitivity to EPU than developed countries, while Belt and Road Initiative countries demonstrate 86% lower sensitivity than non-BRI countries. This research advances EPU–FDI theory by demonstrating how institutional quality creates “policy buffers” against uncertainty and provides policymakers with evidence that strengthening political stability and financial development can maintain investor confidence during uncertain periods, while strategic international partnerships can insulate investment flows from policy volatility. Full article

This study explores the relationship between renewable and non-renewable energy consumption, economic growth (EG), and ecological footprint (EF) in Poland’s top 18 energy-importing countries from 2000 to 2022. While the energy-growth-environment nexus is well-studied, limited attention has been paid to how a single major energy-exporting country influences sustainability in its trade partners, a gap this study aims to fill. A panel dataset was constructed using five key variables: real GDP per capita, Poland’s fuel exports, ecological footprint per capita, renewable energy consumption, and primary energy consumption per capita. Methodologically, the study employs panel cointegration techniques, including FMOL and DOLS estimators for long-run analysis, as well as the VECM and Granger causality tests for the short run. The study’s main contribution lies in its novel focus on Poland’s export influence and the application of advanced econometric models to examine long-run and short-run effects. Results indicate a stable long-run cointegration relationship. Specifically, a 1% increase in renewable energy use is associated with a 0.0219% rise in GDP per capita. Additionally, Poland’s fuel exports and ecological footprint positively impact growth, whereas primary energy use is statistically insignificant. These findings offer practical implications for policymakers in Poland and its trading partners aiming to align energy trade with sustainability goals. Full article

Energy efficiency potentially reduces global carbon emissions, whereas the need of emerging countries to maintain economic growth and development entails a sharp increase in energy consumption. However, to meet this, current energy systems need to be transformed. Several studies find different conclusions on the short-run and long-run relationship and the direction of causality, and none of the studies have considered energy efficiency in their model. This study investigates the direction of causality between energy efficiency, energy consumption, and economic growth in South Africa. To determine if a long-run relationship between the variables exists, the Johanson cointegration test is used, and the results indicate that there is a long-run relationship between economic growth, energy depletion, energy efficiency, non-renewable energy consumption, renewable energy consumption, and energy security, with trace statistics suggesting that the null hypothesis of no cointegration should be rejected at a 5% level of significance. The Toda and Yamamoto procedure of the Granger causality approach was then applied. This study finds a unidirectional causality between energy efficiency, non-renewable energy consumption, and economic growth and no causality between renewable energy consumption, energy depletion, energy security, and economic growth. The growth hypothesis is supported, while the neutrality hypothesis is only confirmed regarding renewable energy consumption and economic growth. The results further suggest that a unidirectional Granger causality exists between non-renewable consumption and energy efficiency, and economic growth in South Africa. In South Africa, energy efficiency is a significant tool to enhance sustainable growth and attain climate objectives. Also, energy efficiency helps to lower the costs of mitigating carbon emissions and further advance both social and economic development. Full article

This study examines how economic growth, travel, global connection, and changes in population impact the environmental footprint in seven countries, including Russia, the US, China, France, the UK, Pakistan, and India, from 1995 to 2023. The results show a significant link between Granger’s environmental impact and some economic, non-economic, and population factors in these countries. According to the study, environmental impacts result primarily from economic expansion and tourism revenue generation. The essential activities in economic development frequently result in significant ecological deficits through natural resource depletion, land alterations, and environmental releases. Business enlargement and tourism income commonly bring about deforestation while causing both pollution and habitat damage, thus showing why sustainable practices must exist to protect nature during economic development. We also have to consider factors other than economics, such as total income from natural resources and using nuclear power early. Additionally, how many people live in a particular area and the number of children born contribute to these footprints. Also, this study shows how economic, non-economic and demographic issues can indicate what harm the environment might face later. This is especially important in countries that use nuclear energy extensively. The report suggests different ways to solve this problem. These include advocating for sustainable tourism practices, directing research efforts towards nuclear energy, supporting renewable energy initiatives, promoting family planning and education, and raising public awareness. The aim is to reduce the environmental harm caused by nuclear energy and promote a more sustainable future. Full article

A correlation between economic development and road freight is demonstrated in the literature review provided in this paper. This relationship was studied in relation to the global gross domestic product (GDP) of the countries under review. Therefore, this paper presents the validation of this correlation in the Colombian case, based not only on global GDP, but also on the GDP for each of the main economic sectors of the country. The correlation was analyzed using several of the following statistical methods: correlation using the non-parametric method (Spearman), the causality relationship using the Granger test, the relationship between variables using Principal Component Analysis (PCA), and multivariate correlation to establish the level of significance of each economic sector by means of the p-value. The study concludes that the best correlation is between the GDP of some economic sectors and the amount of freight transported one year later. Full article

In this study, we analyzed the impact of government regulatory institutions on households’ natural gas use behavior and suggested that the conventional view of natural gas as a social utility is inappropriate. Pursuing this goal, we applied correlation analysis, regression analysis and the Granger causality test to assess the statistically significant impact of particular factors (environmental temperature, price and tariff on natural gas) on household gas consumption. Our study was based on the data on household gas consumption in 2019–2022. Ultimately, the lowest rate of influence was recorded by the Granger causality test (2.47%), compared to 6.88% in the test for the significance of the correlation coefficient and 9.23% in the t-test for the statistical significance of the regression coefficients. One has to note that the Granger causality test used in our study is considered the most sensitive model for analyzing economic data. Using statistical methods, we concluded that regulatory factors have a negligible impact on the volume of natural gas consumption by households. Our results suggest that the Ukrainian regulatory authorities should be cautious about using non-market mechanisms, such as price caps, in the energy sector. Full article

Political events significantly impact economic indices, including agricultural commodities. While Granger causality is a well-established method for analyzing interdependencies between time series data, its traditional application can be challenging to interpret across multiple periods. This research enhances the Granger causality method to quantify changes in the interlinkages among variables over time, offering a more intuitive framework for analyzing how political events affect economic indices. The proposed method involves conducting Granger causality tests across different periods, forming vectors from the results to capture transitions from Granger-causing to non-Granger-causing variables. These vector amplitudes provide quantitative measures of changes with explanatory power over time. The dataset includes eight variables over a decade, focusing on the following major geopolitical events: the Russian occupation of Crimea in 2014 and the invasion of Ukraine in 2022, with an intermediate “no-shocks” period as the reference. The results show significant changes in the interlinkages among the variables during crisis periods compared to stable periods. This enhanced method provides valuable insights, informing trading strategies and risk management during periods of geopolitical instability. This innovative approach offers a novel tool for market participants to better understand and respond to economic shocks caused by political events. Full article

Numerous studies are based on the correlation among stock factors, which affects the measurement value and interpretability of such studies. Research on the causality among stock factors primarily relies on statistical models and machine learning algorithms, thereby failing to fully exploit the formidable computational capabilities of deep learning models. Moreover, the inference of causal relationships largely depends on the Granger causality test, which is not suitable for non-stationary and non-linear stock factors. Also, most existing studies do not consider the impact of confounding variables or further validation of causal relationships. In response to the current research deficiencies, this paper introduces a deep learning-based algorithm aimed at inferring causal relationships between stock closing prices and relevant factors. To achieve this, causal diagrams from the structural causal model (SCM) were integrated into the analysis of stock data. Subsequently, a sliding window strategy combined with Gated Recurrent Units (GRUs) was employed to predict the potential values of closing prices, and a grouped architecture was constructed inspired by the Potential Outcomes Framework (POF) for controlling confounding variables. The architecture was employed to infer causal relationships between closing price and relevant factors through the non-linear Granger causality test. Finally, comparative experimental results demonstrate a marked enhancement in the accuracy and performance of closing price predictions when causal factors were incorporated into the prediction model. This finding not only validates the correctness of the causal inference, but also strengthens the reliability and validity of the proposed methodology. Consequently, this study has significant practical implications for the analysis of causality in financial time series data and the prediction of stock prices. Full article

Forecasting construction spending is important for civil engineering practitioners to make business decisions. Currently, the main body of forecasting literature pertains exclusively to aggregate construction investment, such as total construction spending (TTLCON), private construction spending, or residential construction spending. But type-specific construction spending, such as that for education, healthcare, and religion, had yet to be explored using forecasting techniques. This case study presents a viable procedure by which aggregate and type-specific non-residential construction can be forecasted. The procedure that involves the use of the Granger causality test and the Vector Autoregression (VAR) model proved to be able to provide an accurate forecast pre-COVID-19, with some accuracy even during the COVID-19 pandemic period. Lessons learned include the following: (1) effort should be diverted towards model interpretation, as the impulse–response trial yields results conforming to current well-established empirical evidence; (2) a type-specific approach should be adopted when analyzing construction spending, as different types of construction spending react differently to potential indicators; and (3) complex models incorporating multiple indicators should be used to generate a forecast, as a complex model has a higher chance of containing parameters explanatory of the target variable’s features during the testing period. Full article