This study develops an integrated panel econometric framework for modeling investment–output dynamics in circular economy sectors, explicitly addressing dynamic propagation, long-run equilibrium relationships, endogeneity, and nonlinear responses. Building on the Samuelson–Hicks Multiplier–Accelerator model, the analysis combines two complementary approaches. A dynamic panel specification estimated by the Generalized Method of Moments (Arellano–Bond) is employed to capture output inertia, intertemporal transmission of investment shocks, and stability properties of the dynamic system. In parallel, a nonlinear panel ARDL model estimated using the Pooled Mean Group (PMG/NARDL) methodology is used to identify cointegration and to distinguish between the long-run and short-run effects of positive and negative investment variations. The empirical analysis relies on a balanced panel of 28 European economies (EU-27 and the United Kingdom) over the period 2005–2023, using sectoral circular economy data, with gross value added as the output variable and gross private investment as the main regressor. The results indicate the existence of a stable cointegrated relationship between investment and output, characterized by significant asymmetries, with expansionary investment shocks exerting larger and more persistent effects than contractionary shocks. Dynamic GMM estimates further confirm delayed investment effects and a stable autoregressive structure. Overall, the paper contributes to mathematical economic modeling by providing a unified dynamic–equilibrium panel framework and by extending the empirical relevance of Multiplier–Accelerator dynamics to circular economy systems.

This study analyzed the evolution of the poverty rate between 2004 and 2023 in 29 European countries, using two categories of variables: institutional variables (Corruption Control Index and Rule of Law Index) and economic variables (unemployment rate, shadow economy, government expenditures on social protection and the Gini index). The methodology adopted included dynamic panel econometric models, applying a technique which involves the elimination of individual effects by a primary differencing of the variables and the use of the generalized method of moments (GMM) to evaluate the estimators. This methodology eliminates endogeneity caused by including the dependent variable with lag among the explanatory variables in the model. The results showed a strong negative correlation between the poverty rate and institutional variables, suggesting that improvements in governance and access to education and health resources are essential for poverty reduction. The shadow economy has also been identified as a poverty buffer, providing support in the absence of formal employment opportunities. The short-term impact of government expenditures on social protection was not significant, indicating the need for further analysis to better understand these dynamics. This research can make a significant contribution to the design of more effective public policies aimed at reducing shocks, reducing inequality and promoting sustainable economic growth.

This paper analyzes the volatility relationship between the Romanian BET index and the U.S. SFT-500 index during the period 2019–2024, with a particular focus on the impact of political and geopolitical shocks. The study investigates whether financial markets in emerging economies react symmetrically or asymmetrically to external shocks originating from mature markets, especially during periods of political uncertainty. The research period includes four major systemic events: the COVID-19 pandemic, the military conflict in Ukraine, the 2024 U.S. presidential elections, and the 2024 Romanian elections, all of which generated significant volatility in global markets. The methodological approach combines time series econometrics with the Impulse Indicator Saturation (IIS) technique to identify structural breaks and outliers, without imposing exogenous assumptions about the timing of events. The econometric model includes autoregressive and lagged exogenous variables to estimate the influence of the SFT-500 index on the BET index, while IIS variables capture unanticipated political and economic shocks. Additionally, a Fractionally Integrated GARCH (FIGARCH) specification is applied to model the persistence of volatility over time, capturing the long-memory behavior often observed in emerging markets like Romania. The results confirm a statistically significant but partial synchronization between the two markets, with lagged and contemporaneous effects from the SFT-500 index on the BET index. Volatility in Romania is markedly higher and longer-lasting during domestic political episodes, confirming that local factors are a primary source of market instability. For investors, this underscores the need to embed political risk metrics into emerging market portfolios. For policymakers, it highlights how stronger institutions and transparent governance can dampen election- and crisis-related turbulence.

This study investigates the complex interactions between operational and financial variables that influence infrastructure investment in Romania’s wastewater sector, a serious area for environmental sustainability and compliance with European environmental standards. Using a comprehensive dataset covering all regional water service operators, accounting for over 90% of the country’s population, across the period 2012–2022, the analysis employs dynamic panel data methods to examine how treated wastewater volumes, operating expenditures, revenues, tariffs and the number of connected users shape investment decisions and the modernization of wastewater infrastructure. The results reveal strong links between operational performance and investment behavior, indicating that revenues from wastewater services and efficiency in organic pollutant removal (CBO5) are key determinants of capital investment. The findings further show that an expanding user base generates substantial investment needs, supporting the extension and upgrading of wastewater treatment networks. By combining a dynamic econometric framework with a comprehensive national sample, the study makes an original contribution to the literature by providing robust empirical evidence on the interaction between financial performance, operational efficiency and sustainability objectives in the wastewater sector. The results underscore the importance of good financial planning, strategic management and sustainable investment policies, and offer relevant insights for policymakers, utility operators and researchers concerned with improving wastewater infrastructure performance under current economic and environmental challenges.