In an effort to ensure sustainable consumption and production patterns, recycling has been recognized as one key strategy. With technological advancements further enabling more efficient material recovery, recycling is set to emerge as an influential force in global metal markets, reshaping traditional supply-demand dynamics. To better understand the role of secondary production and its interdependencies within these markets, we employ structural vector autoregressive (SVAR) models to simultaneously analyze the relationships between primary and secondary supply, demand, convenience yield, and futures prices from 2010 to 2023. The analysis highlights the critical role of recycling in the aluminum and lead markets, significantly influencing prices, consumption and, convenience yield, helping to mitigate scarcity and lower commodity prices. In contrast, the dynamics of the copper market are dominated by primary producers, reflecting their larger share of total production volume. While primary producers struggle to adapt to market changes quickly, our findings show that secondary production is highly responsive, particularly to fluctuations in futures prices. This responsiveness highlights the ability of recyclers to increase production volumes when market conditions become more profitable. Our findings underscore the contribution of recycling to an efficient market and emphasize its importance in a sustainable and circular metal industry. • Secondary production is found to significantly influence market dynamics. • Recyclers are highly responsive, particularly to fluctuations in futures prices. • Influence is greater in markets with a higher level of recycling implementation.

This study scrutinised the linkages between insecurity, government expenditure, and income levels in Nigeria. To achieve this objective, data were collected from 2012Q1 to 2023Q2. For the data analysis, vector autoregression (VAR), impulse response functions (IRFs), and a pairwise Granger causality test were deployed. After testing for unit root and diagnosing the model for serial correlation and stability, the study proceeded with the VAR estimation. The independent variables were the Global Peace Index, the number of fatalities, and government expenditures on internal security and defense, while the real GDP was used as the dependent variable to measure income levels. The variables were incorporated into the Cobb-Douglas production function; hence, capital and labour were used as control variables. The Impulse Response Functions (IRFs) indicated that real gross domestic product responded negatively to the Global Peace Index rating and the number of fatalities due to security threats. On the other hand, RGDP responded positively to government expenditures on internal security and defense. The results of the Granger causality test showed there was a one-way causality from the independent variables to RGDP, implying that the independent variables were significant determinants of income levels in Nigeria. It was concluded that insecurity could distort income levels. Granger causality test results showed there was a single directional causal flow from the independent (explanatory) variables to RGDP, implying that the explanatory variables were significant determinants of income levels in Nigeria. As a result, beyond condemnations and reassurances, the government must investigate the underlying causes of insecurity in order to come up with a long-term remedy.

Improving inference and forecasting in VAR models using cross-sectional information

Social relationships are central to well-being because they fulfill social affiliation needs. To explain how social needs are regulated, theories describe daily-life processes among social desire, social contact, and affect. Still, these processes remain empirically underexplored because of their complexity. In this study, we estimated multivariate associations of social desire and affect with social contact across different modalities (in-person, digital), time scales (hourly, daily), and levels of analysis (between-person, contemporaneous, temporally lagged). Participants from two age-heterogeneous samples answered experience sampling questions and contributed data through unobtrusive smartphone sensing, with roughly hourly assessments across 2 days (N = 303) and daily assessments across 14 days (N = 377). Multilevel vector autoregressive network models revealed associations between social contact, social desire, and affect across levels of analysis. Results were highly specific to the examined time scale. When measured at an hourly timescale, people desired more social contact than usual when they engaged in more in-person contact, and higher social desire predicted more future social contact in both experience sampling and smartphone sensing. In contrast, at a daily timescale, social desire did not predict future contact. Bidirectional linkages of affect and social contact were also much denser hourly (vs. daily). Compared with in-person contact, calls and communication app usage generally showed distinct associations with affect. We discuss theoretical implications for the dynamic regulation of social needs, especially regarding homeostatic temporal processes and the role of positive affect in predicting social contact. Finally, we delineate future directions of multimethod research into daily-life social dynamics. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Mental defeat-negative self-appraisals in relation to pain-has been linked to greater pain interference, disability, and suicide risk in chronic pain populations. However, little is understood about how fluctuations in mental defeat shape daily pain experiences and responses. This study applied network analysis on time series data from an experience sampling study to examine these dynamic within-person relationships in 137 adults (M age = 41.9, 84.7% female) with chronic noncancer pain, who completed online surveys 3 times daily over two 7-day periods spaced 6 months apart. Surveys captured in-the-moment ratings of mental defeat, pain (intensity, impacts, medication use), mood, stress, compassion (to self and others), attention (to pain, inward/outward, body/mind), and activity engagement (physical, social). Multilevel vector autoregression models generated temporal and contemporaneous networks, with stability verified through comparison with 1000 simulated models. The temporal network revealed 71 significant edges (top 25% edge weights: 0.07-0.15), showing that increases in mental defeat predicted subsequent increases in attention to pain and perceived pain impacts on self, relationships, and future. In turn, perceived pain impacts on routine and future predicted lower physical activity engagement. The contemporaneous network identified 62 significant edges (0.12-0.37), indicating that mental defeat was connected with increases in attention to pain and perceived pain impact on future, independent from the effects of pain, stress, and mood. Simulation studies confirmed high network stability. These findings offer insights into the interplay between mental defeat and cognitive, emotional and behavioural pain responses. They support the development of just-in-time interventions targeting mental defeat for pain management.

Africa's energy security remains fragile amid unreliable supply, high import exposure, and climate shocks, even as the continent pursues a low carbon transition. This study examines how climate-oriented finance and renewable energy deployment relate to energy security in 16 African countries from 2000 to 2021 using a Panel Vector Autoregression framework. To ensure conceptual clarity, external financial inflows, foreign direct investment and mitigation related development finance, are distinguished from renewable energy consumption as an energy system outcome. Energy security is measured using energy import dependence, energy intensity, and energy productivity. Results show that shocks to renewable energy consumption are followed by medium run improvements in energy intensity and productivity, alongside volatile short run dynamics. In contrast, foreign direct investment exhibits weak average effects in pooled models, becoming more evident when conditioning on institutional quality and income groups. Robustness checks using alternative finance proxies and stratified models confirm the main patterns. The findings are interpreted as time ordered dynamic relationships rather than structural causal effects. • Reframed abstract and introduction to foreground Africa’s energy security and financing constraints, linking strategy. • Expanded and structured contributions across theory, empirics, and policy, distinguishing finance flows and renewables. • Strengthened theory and empirics by integrating development and energy theories, synthesizing African literature, and justifying. • Improved transparency by motivating Panel VAR, comparing alternatives, and interpreting dynamics against theory and evidence.

Groundwater constitutes a critical supply for domestic, agricultural, and industrial needs. Understanding the dynamic interplay between rainfall, river discharge, and groundwater levels remains challenging due to the complexity of hydrogeological structures and variable recharge conditions. This study investigates the temporal relationships among rainfall, river stage-discharge, and groundwater levels in the Erbil Basin, North Iraq. A multivariate time series framework was employed for the data series spanning 2004-2022, combining wavelet coherence analysis to identify dominant periodicities and temporal correlations, with a vector autoregression (VAR) model to forecast future groundwater fluctuations. The wavelet analysis revealed strong monthly-scale variability during the wet season and a pronounced rainfall-groundwater coherence with a dominant 1-month periodicity, particularly after 2008. Spatial differences in coherence strength across monitoring wells indicate regionally consistent hydrogeological conditions, with stronger coupling observed in wells situated closer to the Lesser Zab River. The impulse response function (IRF) analysis additionally demonstrated positive groundwater responses within 2-6 months following rainfall events, consistent with regional recharge patterns. However, persistent groundwater declines identified in the VAR forecasting model reflect the combined effects of reduced recharge and continuous abstraction pressures. The integrated approach provides a comprehensive assessment of hydroclimatic interactions and groundwater dynamics in the Erbil regional aquifer. The findings emphasize the need for adaptive groundwater management strategies and demonstrate the potential of combining wavelet and VAR models for sustainable water resource planning in data-limited, semi-arid environments.

The growing use of intensive longitudinal studies has heightened demand for advanced modeling approaches to better understand temporal dynamics of individual change. Although widely used multilevel vector autoregressive models can capture both intraindividual dynamics and interindividual differences, current implementations are restricted to two-level analyses. This can be problematic, as three-level nested structures are common in practice-for instance, individuals may be further clustered within higher level units, or weekday assessments are nested within weeks that are further nested within individuals. Existing two-level frameworks cannot adequately model such hierarchies, potentially leading to incorrect estimation and inferences about intraindividual dynamics. To address this issue, we introduce a three-level vector autoregressive modeling framework and provide corresponding Bayesian estimation algorithms. We evaluate the estimation performance of three key model specifications-unconditional univariate, conditional univariate (with Level 2 and Level 3 covariates), and unconditional bivariate-through a set of simulation studies. By varying sample sizes at all three levels and key population parameters, these simulations offer initial evidence on how sample sizes across levels influence model performance. An empirical example using ratings of children's daily emotional states during their first kindergarten month illustrates the three-level vector autoregressive framework's utility. We conclude by summarizing key findings, offering practical guidance for application, and pointing out limitations and future directions. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

This study analyzes the determinants of economic growth in Angola, with a particular focus on the composition of public spending and its interaction with key macroeconomic variables. Methodologically, the research uses a Vector Autoregressive (VAR) model as the main empirical strategy, allowing the capture of dynamic interdependencies between Gross Domestic Product (GDP), different types of public spending, the exchange rate, the price of oil, and tax revenue. The variables were transformed into natural logarithms and tested for stationarity using the augmented Dickey-Fuller test and subsequently differentiated to ensure adequate time series properties. The results reveal that the effects of public spending on economic growth vary significantly depending on its composition. Public investment has the most robust positive impact, while spending on wages and pensions has moderate effects, and spending on goods and services demonstrates lower economic efficiency. Additionally, the price of oil emerges as a determining variable, reinforcing the external vulnerability of the Angolan economy. The results suggest that the sustainability of economic growth depends not only on the volume of public spending, but above all on its composition, making it crucial to prioritize spending with a greater multiplier effect

In the framework of Integrated Information Theory, different measures have been proposed to quantify the information integrated in a dynamic system composed by interconnected units, each capturing a different aspect of the system connections. While integrated information measures have been traditionally applied to brain dynamics, in this study we propose their implementation for the investigation of the short-term regulation of cardiovascular (CV) and cardiorespiratory (CR) systems reflected by the spontaneous variability of heart period (HP), systolic arterial pressure (SAP) and respiration (RESP). We selected two integrated information measures related to well-known dynamic properties of CV and CR systems: Whole-Minus-Sum (WMS) quantifying the information stored in the whole system above and beyond the information stored in its units, and causal density (CD) that handles integration in terms of the information transferred between the system units. WMS and CD measures were computed from the parameters of vector autoregressive models fitted respectively on the bivariate time series of {HP, SAP} and {HP, RESP}, measured from healthy young subjects at rest, during head-up tilt and during a mental arithmetic task. We found that CV and CR systems dynamically integrate significant amounts of information, which undergo modulations in response to the tasks performed, especially during tilt, proving that orthostatic stress significantly reduces the degree of cooperation between the parts of the systems. A different response to stress was observed for CD and WMS measures, related to the changes in the information storage and transfer induced by the orthostatic and mental challenges and their physiological interpretation. The possibility offered by the integrated information measures, in combination with their constituent contributions, allowed us to disentangle in a comprehensive way the statistical relationships underlying the CV and CR dynamics, and to investigate for the first time the concept of integration in the cardiovascular and cardiorespiratory systems.

ABSTRACT Climate-driven variability is altering Taiwan’s marine ecosystems, with implications for Portunid crabs (Charybdis feriatus, Portunus pelagicus and Portunus sanguinolentus). This study assessed species-specific exposure to climate-driven changes using an exposure index (Ex) derived from five key environmental variables: chlorophyll-a (Chl-a), sea bottom temperature (SBT), sea surface salinity (SSS), sea surface height (SSH) and sea surface temperature (SST). Fishery-dependent data (2013–2022) were standardized using the Vector Autoregressive Spatio-Temporal (VAST) model, while individual exposure scores for each environmental variable were weighted by variable importance in projection (VIP) through partial least squares regression (PLSR). Results showed that exposure was strongly seasonal and species-specific; high exposure index (Ex ≥ 0.6) occurred mainly in spring – summer, overlapping with spawning periods and peak fishing effort, compounding risks to recruitment and population stability. Exposure differed among species, with SSH carrying the greatest weight for C. feriatus and P. sanguinolentus, and SSS most prominent in winter, while SST, SSS and SSH carried the greatest weight for P. pelagicus. Among climate indices, Niño 3.4 aligned positively with winter – spring exposure (most evident for C. feriatus). The NPO provided short leads (1–4 months) to higher Ex, while the PDO co-varied with exposure in spring and summer, especially for C. feriatus and P. sanguinolentus. Overall, understanding how climate variability raises exposure is essential for adaptive management; real-time monitoring to trigger short closures during vulnerable periods can protect recruitment and support SDG-14.

We present a Bayesian vector autoregressive (BVAR) model designed for panel data. In small-area applications, traditional vector autoregressive (VAR) models quickly become overparameterized, and standard BVARs often rely on aggressive global shrinkage, risking over-shrinking meaningful regional dynamics. To address these challenges, we develop a spatial BVAR-CAR framework that combines global regularization with flexible local shrinkage. We introduce a conditional autoregressive prior on region-specific intercepts to capture spatial dependance and a hierarchical shrinkage (horseshoe-type) prior on autoregressive coefficients to borrow strength across regions, stabilizing estimation in high-dimensional settings. This setup eliminates redundant parameters while retaining heterogeneous dynamics across local areas. We evaluate forecasting performance using two annual panel datasets: average hourly earnings of production employes in California Metropolitan Statistical Areas (small areas) and gender unemployment gaps in African countries. Our BVAR-CAR model outperforms three benchmarks – a univariate AR(1) model, a restricted VAR model with shared hyperparameters, and an unrestricted BVAR without spatial and global-local shrinkage priors – in both panels. The results highlight the benefits of spatial pooling in Bayesian models when time series are short. By integrating cross-sectional structure with temporal dependance, our approach provides a flexible and interpretable solution for forecasting and small-area estimation in regional economic analysis.

ABSTRACT We quantify the economic impact of Russia's invasion of Ukraine on Germany, the United Kingdom, France, and Italy using data on historical geopolitical events. Employing a structural VAR approach based on zero, sign, and narrative sign restrictions, and accounting for regional shocks to capture country‐specific effects, our analysis reveals a drag on real economic activity and a significant increase in inflation due to the war. In a counterfactual scenario without the invasion, we find that consumer prices in European countries would have been 2.3% to 5.8% lower in 2023‐Q4. Additionally, global consumer prices would have risen 2.5% less, and energy prices would have been 24% lower. The war also triggered a significant decline in both global and local economic sentiment, resulting in lower consumer confidence and reduced global demand. Without the war, GDP in European countries could have been between 1.4% and 2.4% higher by the end of 2023.

Various cognitive-affective and behavioral factors contribute to the maintenance of low back pain, but these factors are often evaluated in isolation. The network approach provides an avenue to evaluate multiple cognitive-affective and behavioral factors at once and across time. This study explored the relationship between pain, expectancies, avoidance, and related cognitive-affective and behavioral factors using network analysis. A total of 30 individuals with chronic low back pain completed ecological momentary assessments regarding their physical symptoms and related cognitive-affective and behavioral factors 5 times per day for a duration of 2 weeks. Temporal and contemporaneous networks were estimated using vector autoregressive models on a group and individual level. Networks show that the relationship among cognitive-affective and behavioral factors reinforces each other within the same time frame as well as across time. The strongest temporal connections were found between pain and attention, while the strongest contemporaneous connections were found between fear and attention at the group level. On an individual level, different variations of network structures can be found, suggesting that a more individualized approach is needed for better treatment outcomes. Further research is warranted to confirm the causal relations between cognitive-affective and behavioral factors in the maintenance of chronic pain. The network approach may be a useful tool to develop more effective individualized treatment to manage chronic pain.

ABSTRACT This study employs a time-varying parameter vector autoregressive (TVP-VAR) model to extend the joint connectivity model and investigate the correlation between artificial intelligence (AI) tokens and the energy market. The analysis covers the period from 1 November 2019, to 20 May 2024, and includes eight mainstream AI tokens (such as Cortex, Fetch.ai, and SingularityNET) as well as energy assets including oil, natural gas, and biofuels. The findings reveal a significant correlation between AI tokens and energy assets, with the strength of this correlation exhibiting asymmetric characteristics. This study provides key insights for investors and policymakers to gain a deeper understanding of the evolving interplay between AI-driven digital assets and the energy sector.

ABSTRACT This paper investigates exchange rate pass-through (ERPT) to inflation in Armenia using reduced-form estimation and a structural VAR model. Using monthly and quarterly data from 2008–2023, it provides estimates of both average and shock-specific ERPT. The results reveal substantial heterogeneity across CPI components, with stronger pass-through for tradables and imports. ERPT primarily occurs through the U.S. dollar exchange rate, supporting the dominant currency paradigm. Monetary policy shocks generate the highest pass-through, underscoring the exchange rate channel’s importance in emerging economies. The findings offer broader insights for small, dollarized economies navigating inflation targeting under external volatility and credibility constraints.

This paper introduces novel threshold factor-augmented vector autoregressive (FAVAR) models which extend the conventional FAVAR model to allow for the threshold effect. We develop economically sensible identification conditions and propose a method for estimating threshold values, latent factors and regime-dependent parameters. We also study the estimation of impulse response functions using external instruments and provide a bootstrap procedure to compute their confidence intervals. Asymptotic theories are established. Monte Carlo experiments show good finite sample performance. In empirical applications, we investigate the performance of a threshold FAVAR which employs industrial production growth to determine boom and recession regimes. It outperforms the alternative models in forecasting, and suggests that the monetary policy shocks identified using orthogonalized monetary policy surprises have significantly weaker effects in recessions, on certain macroeconomic variables especially the real ones.

This study empirically examines the economic and environmental factors affecting investment dynamics in Brazil from 1990 to 2021. Using data from IPEADATA, IBGE, and SEEG, we analyze key indicators including investment, capacity utilization, profit share, and CO2 emissions. Unit root tests indicate the series are integrated of order one, supporting the use of a five-lag Vector Autoregression (VAR) model after confirming stationarity in first differences. While the VAR residuals are non-normal, homoscedasticity and structural stability validate dynamic inferences. Impulse response analysis shows that positive shocks to profit share have a lagged, transitory effect on investment, reflecting structural constraints and climate-related financial risks. CO2 emission shocks initially reduce investment, followed by recovery, highlighting sectoral adjustments and regulatory uncertainty. Capacity utilization shocks provoke an immediate investment increase, followed by oscillations, indicating intertemporal adjustments and financial frictions. These findings reveal the intricate interplay between economic performance and environmental pressures, emphasizing the importance of public policies that combine profitability incentives with coordinated green investments. The research contributes to understanding investment behavior under environmental constraints, offering insights aligned with Post-Kaleckian perspectives and the transition to a low-carbon economy.

Dynamic associations between mothers' and fathers' parenting behaviors and infant physiological emotion regulation.

Agriculture has the nature of serving as both a carbon source and carbon sink. Exploring the net carbon effect of agriculture in Guizhou Province and its coupling effects with economic development， as well as analyzing the historical changes and future trends of factors affecting net carbon amount， is of great value for promoting Guizhou Province's agriculture sector to achieve the "dual carbon" goals. In this study， we calculated the net carbon effect of agriculture in Guizhou Province from 2005 to 2022， explored the coupling state changes between the net carbon sink of agriculture and agricultural output value in Guizhou Province using the Tapio decoupling model， decomposed the driving factors of the net carbon sink of agriculture in Guizhou Province based on the logarithmic mean Divisia index （LMDI） model， and further analyzed the dynamic relationship between influencing factors and agricultural net carbon sink using the vector autoregressive （VAR） model to predict the agricultural net carbon sink. The results show that： ① During the study period， Guizhou Province's agriculture exhibited net carbon sink characteristics， with corn and rice being the main carbon sinks， while livestock and poultry farming were the main carbon emission sources. ② The coupling effect between Guizhou Province's agricultural net carbon sink and agricultural output value exhibited two states： economic/ecological dominant coupling and ecological weakening coupling. ③ The level of agricultural economy promoted the agricultural net carbon sink， while the intensity of agricultural net carbon sink， the scale of agricultural labor force， and the agricultural industrial structure all inhibited the agricultural net carbon sink， with decreasing effects in order. ④ From a dynamic perspective， after being impacted by four influencing factors， the agricultural net carbon sink exhibited repeated fluctuations of positive and negative effects over a certain period. From 2023 to 2030， Guizhou Province's agricultural net carbon sink will basically remain around 400×104 t， showing a characteristic of "low-level stabilization." Based on this， it is proposed that the development of low-carbon agriculture in Guizhou Province should be facilitated by realizing the economic value transformation of agricultural carbon sinks， reasonably adjusting the agricultural economic development model， and optimizing labor resources.