Search Results (107)

This paper evaluates the macroeconomic and financial determinants of the money demand of India from 1996: Q1 to 2024: Q4. The paper utilizes the Autoregressive Distributed Lags (ARDL) bounds testing framework and an Error Correction Model (ECM) to estimate the long-run equilibrium and the short-run dynamics of monetary aggregates, narrow money (M1) and broad money (M3). The empirical findings confirm a stable, singularly cointegrated relationship between real money balances (M1, M3), real income (GDP), opportunity cost (91-Day Treasury Bill Rate), and equity wealth (BSE Sensex). Since M1’s income elasticity is 0.53 and M3’s is 0.98, the traditional transaction motives dominate both M1 and M3. The interest rate exerts a negative substitution effect on M1; however, M3 remains structurally safeguarded against short-term fluctuations. Equity market valuations exhibit statistical insignificance across all variable specifications, indicating that the equity market fluctuations do not systematically destabilize long-run money demand. The ECM results reveal a short-term adjustment speed of 17.92% and 8.37% per quarter for M1 and M3, respectively. These findings establish that M3 acts as a robust and stable measure of the Reserve Bank of India’s long-term monetary targeting, driven predominantly by the fluctuations in real fundamental macro variables. Full article

Transportation infrastructure plays a fundamental role in shaping sustainable urban and regional development by improving accessibility, strengthening territorial cohesion, and supporting long-term economic resilience. While previous studies have primarily examined this relationship through macroeconomic indicators and secondary datasets, limited attention has been given to the perceptions of transportation professionals directly involved in infrastructure planning, implementation, and management. Addressing this gap, the present study investigates how transportation professionals in Greece perceive the contribution of transport infrastructure to sustainable regional development within the European policy context. A quantitative, cross-sectional design was employed, administering a structured questionnaire to 384 transportation professionals in October–November 2025. The study examined four interrelated dimensions: socio-economic contribution, environmental sustainability, infrastructure resilience, and long-term development. Multiple regression analysis showed that all four dimensions are significantly and positively associated with perceived regional development, with socio-economic contribution (β* = 0.359) and long-term development (β* = 0.304) showing the largest coefficients (R² = 0.597, p < 0.001). The study contributes practitioner-grounded evidence from the Greek context, with implications for the European Green Deal, TEN-T, and EU Cohesion Policy. Full article

Container sea–rail intermodal transport is pivotal to the low-carbon transformation of global supply chains. However, traditional performance evaluation systems are prone to circular reasoning fallacies due to the nesting of input and output indicators and frequently suffer from spurious regression when analyzing high-dimensional macro time series under small-sample constraints. To address these endogeneity and attribution challenges, this study proposes a four-step progressive causal inference framework. Taking Tianjin Port—a pioneering hub of China’s “road-to-rail” freight restructuring policy—as the empirical subject, we use quarterly operational data covering a complete cycle from 2017Q1 to 2024Q4. First, we construct a strictly exogenous high-quality development index based on turnover efficiency, logistics cost reduction, and carbon emission mitigation, which completely isolates scale input factors. Second, from an initial pool of 35 operational and macroeconomic indicators, 17 candidate variables are rigorously pre-screened according to statistical consistency and logistics system theory. Third, an adaptive Double Machine Learning (DML) model integrated with leave-one-out cross-fitting is applied to disentangle complex collinearity among variables. The results show that DML effectively eliminates confounding noise, accurately identifies 15 true causal drivers, and excludes spurious correlations such as redundant macro-infrastructure investment. Furthermore, a causally weighted composite index reveals that the intermodal system exhibits strong resilience to global supply chain fluctuations and has undergone a four-stage evolution. Its development momentum has fundamentally shifted from extensive scale expansion to a refined mode driven by the synergy of efficiency and service quality. This study provides a robust methodological paradigm for port performance evaluation and targeted decision support for resource allocation. Full article

We present a unifying thesis which posits that the biological resolution of uncertainty is a fundamental adaptation to entropy’s negative impact on the highly ordered molecular structures required to maintain the living state. These molecular biological adaptations are highly conserved and play a critical role in the survival of the earliest multicellular organisms and the vertebrates thereafter. The imperative to reduce cognitive uncertainty is effected through the dopaminergic Medial Forebrain Bundle (MFB) Reward Prediction Error (RPE) mechanism, or its homologous equivalents, to compute a biological valuation of information. This hypothesis is supported by the central role of the MFB seeking system as the neural substrate of exploratory behavior that leads to the reduction of uncertainty when information is apprehended and cognitively assimilated. We define the human experience of intellectual beauty as the subjective emotional reward that is activated by the MFB seeking system. Accordingly, humans experience intellectual beauty when a high-entropy state of confusion is suddenly resolved into a low-entropy state of insight. In humans, the neuroanatomical substrates of inductive reasoning, inquiry, and the intellectual beauty to which they lead are present at birth. What develops postnatally is synaptic plasticity in the connections among these neurons that is activated in the loving didactic relationship that is established between mother and child during infancy. This dynamic is critically dependent on observational learning on the part of the child. It is supported by the joyful engagement and emotional support of the mother. This provides a paradigm of joy in learning that we propose is the developmental origin of intellectual beauty. This is the reinforcement that maintains inquiring behavior in the search for information that is needed to resist the adverse effects of entropy on life. This paper traces the continuous thread of uncertainty resolution from its phylogenetic origins in associative learning to the intuitive science of early childhood, and ultimately to the highest levels of human inquiry in science, as well as literary, musical and visual arts. The intuitive scientific method gives rise to the collective intelligence of groups, an evolved trait that likely contributed to the success of our hominin ancestors. At the societal level, this collective intelligence scales into the institutional working of markets, driving the macroeconomic price discovery of new information to counter entropy. Importantly, we compare the cost of information across the disparate domains of pharmaceutical drug discovery and the contemporary art market to demonstrate that the imperative to reduce uncertainty manifests as a universal, falsifiable mechanism for the “price discovery” of information. Full article

This study investigates whether shareholder value creation, defined as beta-adjusted outperformance relative to a market benchmark, can be effectively predicted in an emerging market using a sequential machine learning framework. While prior research has predominantly focused on profitability forecasting or stock return prediction, the prediction of risk-adjusted shareholder value creation remains relatively underexplored, particularly in emerging economies such as Morocco. To address this gap, the study develops a predictive framework that combines market-based indicators, macroeconomic variables, and accounting fundamentals using only information realistically available to investors at each decision date. These variables are organized into firm-level temporal sequences based on a monthly decision-date panel of non-financial firms listed on the Casablanca Stock Exchange over the period 2010–2024. To capture nonlinear relationships and temporal dependencies in financial data, the empirical analysis compares baseline models with deep learning architectures, including GRU, LSTM, and CNN1D. The results indicate that deep learning models consistently outperform naïve and linear benchmark models, suggesting that shareholder value creation exhibits a measurable degree of predictability. With an AUC of 0.700 and a PR-AUC of 0.727, CNN1D achieves the strongest performance in the final evaluation setting and ranks as the best-performing model according to the primary AUC criterion. The findings also reveal that macroeconomic variables generate the strongest standalone predictive signal, whereas market-based variables exhibit comparatively weaker predictive power when considered in isolation. By extending financial prediction toward a risk-adjusted, benchmark-based, and investor-oriented framework, and by providing new empirical evidence on the value of temporal modeling and multi-source financial information for forecasting shareholder value creation in an emerging market context, this study contributes to the growing literature at the intersection of financial forecasting and artificial intelligence. Full article

This paper investigates the macroeconomic determinants of Romania’s budget deficit over the post-EU accession period, drawing on quarterly data from Q1 2007 to Q2 2025. Using an Autoregressive Distributed Lag (ARDL) model, we identify both short- and long-run relationships between the fiscal balance and a set of macroeconomic fundamentals. Real GDP growth proves to be the most robust positive determinant, with a short-run coefficient of 0.2718 and a long-run multiplier of 0.5152—a finding that firmly establishes sustained economic expansion as the primary structural force behind fiscal improvement. Inflation is positively associated with fiscal balance, consistent with nominal revenue buoyancy outpacing expenditure indexation in the short run. Public debt exerts a persistent negative effect, confirming that accumulated borrowing generates structural pressure on fiscal outcomes over time. A fiscal persistence parameter of 0.4724 reveals that nearly half of any quarter’s deficit is carried forward from the previous one—a feature that compounds temporary shocks into prolonged imbalances. Bounds testing confirms cointegration, validating the long-run interpretation of the estimated multipliers. The results contribute to the literature on fiscal reaction functions in emerging EU economies, particularly by documenting the interaction between cyclical conditions, external imbalances, and fiscal sustainability. The findings carry direct implications for the long-term sustainability of Romania’s public finances and welfare state. Persistent fiscal deficits and compounding debt dynamics constrain the government’s capacity to finance public investment, social transfers, and the green transition—highlighting the interdependence between fiscal consolidation and broader sustainability goals aligned with sustainable development goals: Decent Work and Economic Growth, Reduced Inequalities, and Partnerships for the Goals. Full article

Autoregressive models with exogenous variables (ARX) constitute a fundamental class of dynamic regression models used extensively for time series analysis across a wide range of applications. A pervasive limitation of the existing Bayesian analyses of ARX models is their near-exclusive reliance on the Gaussian error assumption, which is routinely violated in empirical applications exhibiting heavy-tailed innovations, distributional outliers, or excess kurtosis. To address this deficiency, we develop a rigorous Bayesian estimation framework for these models whose errors are drawn from the scale-mixtures of normal (SMN) family, which is a rich, symmetric, heavy-tailed class of distributions. Exploiting the hierarchical stochastic representation of the SMN family through observation-specific latent scale-mixing variables, the ARX model is embedded in an augmented data structure that restores Gaussian conditional structure. Under three distinct prior formulations—namely, normal-gamma, Zellner’s g-prior, and Jeffreys’ prior—we derive closed-form full conditional posterior distributions for the ARX coefficient vector and the error scale parameter, which follow multivariate normal and inverse-gamma distributions, respectively. In addition, for the SMN-specific shape parameters, we derive the full conditional posteriors for each distribution in the family, and some of them are non-standard distributions handled by embedding Metropolis-Hastings steps within the Gibbs sampler. The resulting hybrid MCMC algorithm is validated through a comprehensive simulation study spanning three ARX model configurations and all three SMN special cases. A real macroeconomic application to US consumer price inflation demonstrates the practical utility of the framework, confirming heavy-tailed residuals and yielding precise, well-calibrated posterior estimates. Full article

This study explores the effect that the adoption of International Financial Reporting Standards (IFRS) 9 has on credit risk in commercial banks in Cambodia, focused primarily on non-performing loans (NPLs) as a significant indicator. In the static and dynamic panel estimations, the analysis shows that the NPL behavior is best characterized using a dynamic specification, which passes relevant diagnostic tests and leads to evidence of persistence and endogeneity, which has not been conducted in Cambodia yet. The study covered the period from 2013 to 2024. During this period, 26 commercial banks had complete datasets. Combining time-series and cross-sectional data, the total sample size was 312 observations. The results show substantial path dependence in NPLs, suggesting credit deterioration is persistent and that early measures are needed. We find evidence that the adoption of IFRS 9 is positively and significantly associated with increased measures of NPLs, though we interpret this as consistent with improved transparency and forward-looking recognition of expected credit losses—and not indicative of deterioration in underlying asset quality. Bank-specific determinants such as profitability, size, leverage, and liquidity emerge as key predictors of credit risk; banks with stronger financial fundamentals experience improved asset quality. Macroeconomic factors like economic growth are key to decreasing NPLs in the dynamic framework as well. The results highlight the need for forward-looking accounting standards, prudent bank-level practices, and macroeconomic stability. Policy issues include increased supervisory vigilance, legal conservatism when assessing IFRS 9-related indicators, a revision of the capital and liquidity regulatory framework in relation to counterparties operating with them, as well as coordinated macroeconomic policies aiming at boosting the financial system—economy arterial connection. Full article

This study examines the determinants of regulated electricity contract prices in Colombia during the period 2009–2021, with a particular focus on the role of electricity-market fundamentals and macroeconomic conditions. Although regulated contracts are designed to reduce exposure to short-term volatility, limited evidence exists on how their price formation behaves across different segments of the distribution. To address this issue, the analysis combines quantile regression with autoencoder-based dimensionality reduction, allowing the incorporation of a large set of macroeconomic variables without overparameterizing the model. The results show that regulated contract prices are more consistently associated with electricity-system factors than with broad macroeconomic conditions. In particular, the spot price becomes significant only in the upper quantiles, where it appears to operate as an indicator of operational stress, while hydropower and thermal generation exhibit localized effects across the distribution. By contrast, most macroeconomic factors display weak, uneven, or non-significant effects, with only the exchange-rate-related component becoming clearly relevant at relatively high price levels. A robustness analysis based on principal component analysis broadly supports these patterns. Overall, the evidence suggests that the Colombian regulated market behaves as a relatively stable contractual system, in which price formation is shaped mainly by electricity-sector conditions, indexation rules, and long-term risk-management mechanisms, while macroeconomic influences appear more limited and non-uniform across quantiles. Full article

This paper examines the macroeconomic determinants of government debt in two small open economies with distinct institutional frameworks—Armenia and Slovenia. The analysis focuses on key fiscal variables (budget balance and GDP growth) and monetary factors (inflation and interest rates). Using quarterly data for the period 2004–2025 and an Autoregressive Distributed Lag (ARDL) approach, the results provide robust evidence of cointegration between government debt and its macroeconomic drivers. The findings reveal distinct debt dynamics across the two countries. In Armenia, debt is predominantly growth-driven: higher GDP growth significantly reduces debt levels, while rising interest rates increase debt burdens, with fiscal balance and inflation showing limited long-run significance. In contrast, Slovenia’s debt dynamics are shaped by Eurozone-constrained monetary and fiscal conditions, inflation persistence, and accession-related structural shifts. While GDP growth, fiscal balance, and inflation have only marginal long-run effects, short-run dynamics are influenced by inflation persistence and the structural impact of Euro adoption. Error-correction mechanisms confirm stable long-run convergence in both models. The results highlight that debt sustainability in small open economies is highly context-dependent, reflecting the interaction between macroeconomic fundamentals and institutional constraints. The study contributes to the literature by offering a comparative ARDL-based analysis and by distinguishing between growth-driven and institution-driven debt regimes, while also providing policy-relevant insights for balancing growth, fiscal discipline, and institutional compliance. Full article

Association rule mining is a fundamental data mining technique for uncovering latent relationships among variables in large-scale datasets. However, conventional approaches rely on single-metric filtering strategies, which are insufficient for capturing the inherent multi-criteria nature of rule quality. To address this limitation, this study formulates ARM as a many-objective optimization problem and proposes a hybrid algorithm, NSGA-II/DE-ARM, that simultaneously optimizes four rule-quality measures: support, confidence, lift, and NetConf. The proposed algorithm enhances the NSGA-II framework by integrating binary differential evolution operators, an adaptive operator selection mechanism, lift-weighted tournament selection, and a constraint-domination principle combined with a dynamic minimum support threshold. Its performance was evaluated using two datasets: a SIPRI–World Bank panel dataset consisting of defense industry and macroeconomic indicators covering 46 items over the 2002–2023 period, and the UCI Mushroom benchmark dataset consisting of 118 items. Across 30 independent runs on the SIPRI–World Bank dataset, NSGA-II/DE-ARM outperformed the Apriori baseline in all four metrics (mean lift = 4.748, confidence = 0.853, support = 0.146, NetConf = 0.789), with large effect sizes (Cohen’s d = 1.77–5.77, p < 0.001 in each case). On the Mushroom benchmark dataset, the proposed method also achieved substantial improvements, with Cohen’s d values ranging from 0.93 to 6.16. NSGA-II/DE-ARM generated 68 Pareto-optimal rules in a representative run and achieved the highest hypervolume values on both datasets, with HV = 3.231 for SIPRI–World Bank and HV = 6.262 for Mushroom. These results suggest that NSGA-II/DE-ARM offers decision-makers a broader and more balanced multi-criteria solution set than single-metric filtering approaches. Full article

Oil price volatility forecasting remains a central challenge in financial risk management and macroeconomic policy, particularly when market uncertainty stems from expert judgment, geopolitical assessments, or imprecisely quantified fundamentals rather than statistical frequencies. We propose a bivariate uncertain vector autoregressive (UVAR) model to jointly forecast crude oil realized volatility (RV) and the Overall Equity Market Volatility (EMV) tracker within the framework of uncertainty theory, using 204 monthly observations from January 2008 to December 2024. Three cross-validation schemes consistently identify UVAR(1) as optimal, and least-squares estimation reveals an asymmetric bidirectional relationship between the two variables. Residual analysis and uncertain hypothesis testing confirm the adequacy of the fitted model at both $\alpha =0.05$ and $\alpha =0.10$, the conventional significance levels reported in the empirical literature. Relative to a univariate UAR benchmark, UVAR(1) yields lower residual variance and, on average, narrower 95% confidence intervals for both variables and remedies the hypothesis-test failure of UAR(1) for realized volatility; while its fixed-origin ATE is marginally higher on the EMV tracker, this is more than offset by substantial gains on realized volatility, the primary economic variable of interest. Against a probabilistic VAR(1) benchmark, UVAR(1) attains marginally lower out-of-sample sum of squared mean errors while uniquely supporting principled uncertain-statistical inference under non-frequentist data-generating mechanisms. These results provide principled inputs for value-at-risk assessment and portfolio hedging in oil-dependent economies. Full article

Energy remains fundamental to economic growth and national development, yet Egypt faces a persistent challenge in expanding energy supply without deepening dependence on conventional sources. Although earlier studies examined the determinants of energy use, limited evidence exists on whether macroeconomic and environmental factors differently affect renewable energy consumption (REC) and non-renewable energy consumption (NREC) in the Egyptian context. This study addresses this problem by examining the roles of economic growth, financial development, the ecological footprint, and economic globalization in shaping REC and NREC in Egypt over the period 1970 to 2024. To achieve this objective, the study employs the Autoregressive Distributed Lag (ARDL) approach, which is suitable for estimating short-run dynamics and long-run relationships among variables with mixed orders of integration. The results indicate that across the REC models, economic growth increases renewable consumption, while the ecological footprint reduces it, indicating that environmental pressure has not translated into stronger REC. Financial development exhibits as a negative in the long run, suggesting finance has not been consistently directed toward renewables. Although economic globalization is insignificant, trade and financial globalization reduce REC in the long run. For the NREC models in the long run, GDP, financial development, and ecological footprint increase NREC. Economic, trade, and financial globalization effects are mostly insignificant for NREC, implying that domestic fundamentals drive conventional energy use. Thus, the findings suggest that Egypt’s energy structure is still driven more by domestic growth and financial conditions than by external integration. However, these results should be interpreted as evidence of association within the ARDL framework rather than proof of causality. The study therefore highlights the need for policies that align economic growth with renewable energy expansion, improve the direction of finance toward green investment, and strengthen the institutional conditions necessary to support a more sustainable energy transition Full article

Digital technologies have fundamentally changed how public administration operates, moving it from traditional bureaucratic structures toward more efficient and responsive systems. This study analyzes the links between e-government usage (measured as the percentage of individuals who interact with public authorities via online platforms), governance quality, and fiscal performance across ten Central and Eastern European countries from 2010 to 2023. Using a fixed-effects panel data model, we investigate whether higher e-government usage is associated with stronger government effectiveness, improved budget balances, and more sustainable public debt levels, while controlling for key macroeconomic and structural factors. Employing a fixed-effects panel data model, we examine whether greater use of e-government services is associated with stronger government effectiveness, improved budget balances, and more sustainable public debt levels, while accounting for key macroeconomic and structural factors. The findings show a positive and statistically significant association between e-government usage and government effectiveness. The links to fiscal outcomes are more nuanced: e-government usage is associated with better budget balances, mainly through indirect channels such as higher tax compliance and tighter expenditure control. In contrast, its association with public debt levels is weaker and appears to depend more strongly on broader macroeconomic conditions. Overall, the findings suggest that greater e-government usage is associated with improvements in governance quality in the CEE region, although its contribution to long-term fiscal sustainability remains conditional on the quality of existing institutions. Full article

Exchange rate volatility has intensified in recent decades, yet its systematic implications for banking-sector stability remain contested. This study investigates whether exchange rate volatility constitutes a meaningful source of financial fragility using a global panel of 103 countries over the period 2000–2021. Financial stability is proxied by the banking-sector Z-score, while exchange rate volatility is estimated using a EGARCH-based framework to capture time-varying uncertainty. To address cross-sectional dependence, heterogeneity, and endogeneity, the analysis employs Driscoll–Kraay fixed effects, two-step system GMM, and quantile regressions. The results reveal that exchange rate volatility exerts a statistically and economically significant negative effect on banking stability, reducing Z-scores across countries and income groups. The findings remain robust across alternative specifications and estimators. Bank-level fundamentals—capitalisation, liquidity, and credit—enhance stability, whereas higher non-performing loans and risk exposure amplify fragility. Macroeconomic conditions also matter, with stronger growth, institutional quality and external balances supporting resilience, while inflation, economic policy uncertainty and expansionary government spending weaken stability. By integrating time-varying volatility modelling with dynamic panel techniques in a large cross-country setting, this study provides new global evidence that exchange rate volatility is not merely a macroeconomic fluctuation but a structural source of banking-sector risk. The findings carry important implications for macroprudential policy, foreign-exchange management, and coordinated monetary–fiscal responses aimed at safeguarding financial stability in open economies. Full article