Search Results (43)

This study investigates the dynamic relationship between maritime trade and cultural convergence between China and Saudi Arabia, with a particular focus on the roles of creative goods and information and communication technology (ICT) exports as proxies for sociocultural integration. Utilizing quarterly data from 2012 to 2021, the analysis employs the Toda–Yamamoto Granger causality approach within a Vector Autoregression (VAR) framework. This methodology offers a robust means of testing causality without requiring data stationarity or cointegration, thereby reducing estimation bias and enhancing applicability to real-world economic data. The empirical model examines causal interactions among maritime trade, creative goods exports, ICT exports, and population, the latter serving as a control variable to account for demographic scale effects on trade dynamics. The results indicate statistically significant bidirectional causality between maritime trade and both creative goods and ICT exports, suggesting a reciprocal reinforcement between trade and cultural–technological exchange. In contrast, the relationship between maritime trade and population is found to be unidirectional. These findings underscore the strategic importance of cultural and technological flows in shaping maritime trade patterns. Furthermore, the study contextualizes its results within broader policy initiatives, notably China’s Belt and Road Initiative and Saudi Arabia’s Vision 2030, both of which aim to promote mutual economic diversification and regional integration. The study contributes to the literature on international trade and cultural economics by demonstrating how cultural convergence can serve as a catalyst for strengthening bilateral trade relations. Policy implications include the promotion of cultural and technological collaboration, investment in maritime infrastructure, and the incorporation of cultural dimensions into trade policy formulation. Full article

This study investigates the impact of digital transactions on sustainable economic growth in Turkey, utilizing a vector autoregressive (VAR) model and quarterly data from 2006 to 2023. The results indicate a positive long-term association between digital payments and GDP. Granger causality tests and impulse response functions suggest a bidirectional relationship, highlighting mutual reinforcement between economic activity and digital financial adoption. The study also investigates three potential transmission channels linking digital payments to economic performance: household consumption, productivity, and financial intermediation. Evidence shows that digital payments are associated with increased consumption and financial sector activity, while the link to productivity is less conclusive. These findings imply that policymakers should prioritize digital financial infrastructure development and enhance regulatory frameworks to promote inclusive and sustainable economic growth. Full article

As the global energy transition progresses, green electricity, which is crucial for low-carbon systems, has gained attention. However, the lack of effective market linkages hinders a full understanding of the price transmission effects across green markets. This study uses the Vector Autoregression (VAR) model and Granger causality tests to analyze the price transmission and lag effects between the carbon, green certificate, and China Certified Emission Reduction (CCER) Markets. The findings reveal complex price linkages, offering theoretical insights and policy recommendations for optimizing green electricity markets and environmental rights trading. Full article

The article analyzes the Granger-based causal relationship between two major crude oil markets, namely Brent and West Texas Intermediate (WTI), by using the standard vector autoregression (VAR) framework. In this regard, the effects of the COVID-19 pandemic and the Russia–Ukraine war on causality between Brent and WTI are examined. The empirical results from Granger-causality tests show (a) strong causality from Brent to WTI during the period prior to the COVID-19 pandemic and Russia–Ukraine war, (b) no causality from WTI to Brent during the period prior to the COVID-19 pandemic and Russia–Ukraine war, (c) no causality from Brent to WTI during the COVID-19 pandemic, (d) evidence of causality from WTI to Brent during the COVID-19 pandemic, and (e) no evidence of causality from both markets during the period of Russia–Ukraine war. In addition, causality tests in quantiles support results from the linear Granger causality tests in general. However, contrary to the standard linear causality test, the quantile-in-regression causality test shows that Brent returns cause WTI returns during the pandemic period and WTI returns cause Brent returns before the pandemic. Furthermore, the results from the time-varying Granger causality tests support all conclusions from the standard linear (and static) Granger causality test, except the hypothesis that Brent causes WTI during the pandemic. Moreover, the time-varying Granger tests show evidence that causality between Brent and WTI clearly varies across the pandemic and war periods. Revealing the causalities between Brent and WTI across periods of economic and political stability, pandemic, and war would help policymakers develop appropriate energy policy and help investors determine appropriate risk management actions. Full article

In the context of current global challenges, the integration of digitalization, financial performance, and renewable energy is pivotal in fostering sustainable and resilient economic development. The aim of this paper is to explore the interplay between banking performance, digitalization, and renewable energy consumption in the context of the European Union (EU), with a focus on sustainable economic development. This study examines the extent to which the digitalization and efficiency of the banking sector influence the uptake of renewable energy considering the EU’s environmental and economic priorities. The methodology used involves an econometric analysis based on statistical data from EU countries, using Fully Modified Ordinary Least Squares (FMOLS) to assess causal relationships between variables, complemented by Vector Autoregression (VAR) models and Granger causality tests to further investigate the dynamic interactions among the variables. The data were analyzed to examine the correlation between banking performance, digitalization, and renewable energy consumption levels. The results reveal a positive correlation between greater digitalization in the banking sector, stronger financial performance, and higher investments in renewable energy sources. These factors also support the transition to a green economy, but the effect varies between EU countries depending on national policies and existing digital infrastructure. Recommendations for policymakers include stimulating digitalization in the financial sector, creating a regulatory framework to encourage green energy investments, and strengthening collaboration between financial institutions and the energy sector to facilitate the transition to renewables. This paper also suggests a fiscal policy conducive to technological innovation and digitalization to accelerate the uptake of renewable energy. Full article

This study explores the relationship between income inequality, economic growth, and unemployment in Greece from 2003 to 2020, encompassing key economic phases: pre-crisis, crisis, memoranda, and post-memoranda. The aim is to analyze how economic growth (logarithm of GDP-LOGGDP) and unemployment influenced income inequality (GINI coefficient) during periods of economic turmoil and recovery. Using linear regression analysis on 18 years of annual data, this study identifies significant relationships between the variables, with diagnostic tests confirming model robustness. The findings reveal a strong positive and statistically significant relationship between LOGGDP and income inequality, indicating that economic growth, without effective redistributive mechanisms, exacerbated disparities. Unemployment had an even stronger positive effect on inequality, highlighting its role in deepening income disparities, particularly during the crisis years marked by economic contraction and austerity measures. These results underline the critical need for balanced economic policies that promote inclusive growth while addressing structural inequalities and labor market vulnerabilities. This study also employs advanced econometric methods, including Vector Autoregression (VAR), Vector Error Correction Model (VECM), and Granger Causality Test, to analyze the dynamics between GDP (LOGGDP), income inequality (GINI), and unemployment. The Granger Test reveals that unemployment Granger-causes GDP with a two-period lag, highlighting the importance of labor market conditions for economic growth, while no direct causal relationship is found between GDP and inequality. These methods provide deeper insights into the short- and long-term interactions, offering valuable guidance for balanced economic policymaking. Full article

This study investigates the integration of the Indonesian stock market with eight major trading partner countries, namely, China, Japan, the United States, Malaysia, India, Singapore, the Philippines, and South Korea. The analysis of the stock-market integration investigation includes the following two main things: short-term and long-term dynamic relationships within the Vector Autoregressive (VAR) model framework based on the unit root test, multivariate Johansen cointegration, and paired Granger causality test. The VAR model was analyzed using weekly closing index data of the Indonesian stock exchange and eight major trading partners from January 2013 to June 2024. The results of the study show that the integration of the Indonesian stock market with those of its main trading partners in the long term is relatively low. This finding implies that investors from the eight major trading partner countries can diversify their portfolios in international investments via the Indonesian stock market and vice versa. In the short term, these results prove that Indonesia’s stock markets and those of its major trading partners are integrated, excluding China. The Chinese stock market has become segmented and more attractive for Indonesian investors who want to benefit from diversification and vice versa. Furthermore, the Indonesian stock market has two-way causal relationships with the US, Japanese, Indian, and Singaporean stock markets. In addition, the Indonesian stock market has unidirectional reciprocal-lagged relationships with Malaysia and the Philippines. An essential contribution of this study is helping policymakers and, especially, international investors understand the dynamic relationships of the Indonesian stock market with its major trading partners. Furthermore, this study contributes to the development of empirical literature on the comovement of the Indonesian stock market and those of its major trading partners, as well as the stock markets of developing and developed countries. 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

Although the movement of people from rural to urban areas has caused the increased use of energy, the abundance of water resources can be made into a form of renewable energy known as hydroelectricity. As European countries are ranked as the first users and exporters of hydropower, the production of renewable energy in developed countries such as the Nordic region has caused great impacts on economic growth and human development. The importance of this paper is to investigate the relationship between hydroelectricity and the Human Development Index by depending on some variables such as urbanization, rule of law, corruption, trade openness, and GDP per capita from 2002 to 2021 in Nordic countries. The results were estimated depending on impulse response function after conducting the Vector autoregressive model (VAR) model and Granger causality test. Results showed a negative impact from hydro plants in the short run but a significant positive impact in the long run in Nordic countries. The long-term sustainment of Human Development Index (HDI) is due to policies limiting the immigration of labor as well as protection of energy use. Water batteries are gaining popularity across Europe and their implementation is near mandatory. Full article

This study examines the dynamic relationship between the business cycle and the construction sector activity in 27 EU countries, focusing on Poland. Using the cross-correlation function (CCF) and a set of economic- and construction-related variables, including GDP growth, construction production, building permits, and construction operating time by backlog, quarterly data from 2000Q1 to 2023Q2 (94 quarters in total) are analyzed. Beyond the CCF analysis, causality is also examined using Toda–Yamamoto tests to explore the nuanced temporal relationships between GDP growth and construction activity proxies. The research uncovers synchronized positive lag max results for construction production, suggesting a harmonized response to broader economic changes, especially within 9 to 11 quarters. In contrast, building permits and construction time by backlog show divergent positive lag max values, suggesting the need for tailored, localized strategies. Negative lag max values emphasize the anticipatory role of the construction sector as an early indicator of economic change. Overcoming methodological challenges, this study provides insights critical for policymakers and researchers, promoting a nuanced understanding of economic synchrony and guiding informed strategies for sustainable development. Future recommendations include refining localized strategies based on lag patterns for optimal economic management. Full article

The demand for renewable energy is increasing globally due to concerns about climate change, pollution, and the finite nature of fossil-fuel resources, and renewable energy has been recognized as a significant factor in realizing sustainable development. The government of Saudi Arabia adopted the reduction in fossil-fuel subsidies policy as a financial motivation for supporting both the production and consumption of fossil fuels. Therefore, this study aims to investigate the influence and shocks of Saudi financial development indicators on renewable energy consumption (REC) and to examine the track of causality between financial development indicators and REC. The study covers the annual data period of 1990–2021 and applies the Basic Vector Autoregressive model (VAR), the Granger causality test, forecast-error variance decomposition (FEVD), and the impulse response function (IRF). In the short run, the VAR results indicate a positive and significant impact of stock price volatility and private credit on REC. The results of causality between REC and financial development indicators were conflicting. The Granger causality test shows significant causality running from stock price volatility and private credit to REC. The FEVD results reveal that REC variation is explained by its innovative shocks and has a positive response to shocks in financial development. The IRF results show that REC has a positive response to shock on private credit, liquid liabilities, and stock price volatility. Authorities can encourage investment in renewable energy consumption by providing financial incentives; also, governments can foster national and international partnerships between investors, policymakers, and industry stakeholders. Employing different determinants of financial development indicators and incorporating population factors in the REC function will be highly recommended for forming the renewable energy demand in Saudi Arabia. Conducting a micro-level analysis of specific sectors within renewable energy, such as solar, wind, and others, can provide actionable insights for policymakers. Full article

The process of deepening the economic integration of European economies reached its peak with the formation of a supranational entity for conducting monetary policy. However, the high degree of financial integration of the market also implied the vulnerability of the economic union in terms of prompt reaction to external shocks with divergent effects. Oil price fluctuations are of essential importance for macroeconomic performance, which is particularly reflected in countries more dependent on the import of this raw material. This research aims to apostrophize the asymmetric effects of oil price fluctuations on the stock market indices on a sample of higher (Germany, Italy, France) vs. lower (Croatia, Bulgaria, Ireland) oil importers. The empirical findings are determined based on impulse response functions derived from the VAR model as well as the Granger causality test of the relationship between stock market indices and oil price fluctuations. In order to identify the isolated impact of oil price movements on stock market indices of selected European economies, the VAR (Vector AutoRegression) model is evaluated in the time period 2013M1-2023M1. The results of the research indicate an asymmetric mechanism of the impact of oil shocks on the financial markets of EU member states. Full article

This paper examines normality in time series econometrics for a sustainable energy transition. By analysing data from January 1997 to December 2021, this study integrates macroeconomic, environmental, and energy data to gain insights into the potential changes in daily commuting patterns among Slovenians. Various methods, including unit root tests such as the augmented Dickey–Fuller (ADF), Kwiatkowski–Phillips–Schmidt–Shin (KPSS), and Zivot–Andrews (Z-A), as well as other tests, are employed. Additionally, the vector autoregressive (VAR) model, Granger Causality and regression analysis determine the impact. This paper contributes to uncovering valuable information within data from macrovariables using macroeconometric techniques. It also provides insights that can support evidence-based decision-making for sustainable energy transition policies in Slovenia. The results of the normality tests indicate that most macro variables are integrated; there is a need for a careful analysis of integration levels and appropriate testing methods. These findings have implications for policymakers, researchers, and practitioners in economics, the environment, and energy supply. At the same time, this research highlights that gross domestic product, unemployment, inflation, and carbon dioxide positively impact car usage among Slovenians, while gasoline prices and commuters have a negative one. While the recently investigated development of sustainable commuting does not work, the study highlights an innovation: the connection of time series econometrics, which offers a better understanding of future commuting patterns on energy consumption and their causalities. Full article

The globalization process and the war in Ukraine show us that migration is one of the strongest global trends in the modern economy. For this paper, we determined three types of migration, depending on the intention of the people involved, these being labor, educational, and refugee migration. Each type has a different influence on the macroeconomic process. However, in this paper, we investigate the influence of general migration on GDP. We analyze five factors that have major influences on GDP, namely, migration (I), interest rate (IR), active population (AP), export (E), and the consumer price index (CPI). For the purposes of this paper, vector autoregressive models (VAR models) were chosen to perform the analysis. We used the Granger causality test to investigate the lag structure and identified the exogenous variables in the VAR model, such as GDP, migration, and the active population. We investigated the cross-influence between these factors and found that migration has a negative effect on the active population and a positive effect on GDP, while GDP growth leads to a decrease in migration. The Akaike and Schwartz criteria showed the high quality of the VAR models. The impulse analysis of shock influences identifies the structure of the reaction seen in GDP and migration, depending on their shock factors. Using decomposition analysis, we found that migration and GDP influence each other by 10–14%, which can improve the forecasting of these factors and the study of structural migration by the use of these three types. Full article

In polder-type land, water dynamics are heavily influenced by the artificial maintenance of water levels. Polders are low-lying areas of land that have been reclaimed from the sea or from freshwater bodies and are protected from flooding by dikes or other types of flood-protection structures. The water regime in polders is typically managed using a system of canals, pumps, and sluices to control the flow of water in and out of the area. In this study, the temporal changes in water salinity in the polder-type agricultural floodplain within the Neretva River Delta (NRD), Croatia, were analyzed by applying multivariate statistics and forecast modelling. The main aim of the study was to test the model that can be used in practice to forecast, primarily, water suitability for irrigation in a coastal low-lying agricultural catchment. The specific aim of this study was to use hydrochemistry data series to explain processes in water salinity dynamics and to test the model which may provide accurate salinity prediction, or finally select the conditions in which the model can be applied. We considered the accuracy of the model, and it was validated using independent data sets. To describe different patterns of chemical changes in different water classes due to their complex hydrological connectivity, multivariate statistics (PCA) were coupled with time-series analysis and Vector Autoregression (VAR) model forecasting. The multivariate statistics applied here did not indicate a clear connection between water salinity of the surface-water bodies and groundwater. The lack of correlation lies in the complex hydrological dynamics and interconnectivity of the water bodies highly affected by the artificial maintenance of the groundwater level within the polder area, as well as interventions in the temporal release of freshwater into the drainage canal network. Not all individual water classes contributed equally to the dominant patterns of ionic species identified by PCA. Apparently, land use and agricultural management practices in the different polders lead to uneven water chemistry and the predominant contributions of specific ions, especially nutrients. After applying the Granger causality test to reveal the causal information and explain hidden relationships among the variables, only two surface-water and two groundwater monitoring locations displayed a strong causal relationship between water electrical conductivity (EC_w) as an effect and sea level as a possible cause. The developed models can be used to evaluate and emphasize the unique characteristics and phenomena of low-lying land and to communicate their importance and influence to management authorities and agricultural producers in managing and planning irrigation management in the wider Mediterranean area. Full article