Purpose Unpredictable construction material costs pose a major challenge for contractors, developers, and policymakers in Egypt. This study develops a forecasting framework for the Construction Material Cost Index (CMCI) that integrates material prices with key macroeconomic indicators. The aim is not only to predict cost fluctuations but also to provide an interpretable, context-specific tool for supporting budgeting, procurement, and risk management in volatile economies. Design/methodology/approach Using a Vector Autoregression (VAR) approach, the study analyzed monthly data from 2012 to 2022 covering five construction materials (steel, cement, bricks, gypsum board and ceramic tiles) and five macroeconomic indicators (GDP, CPI, ER, M2, and IR). A suite of statistical tests—correlation analysis, Augmented Dickey-Fuller (ADF) test, and Granger causality—was applied to identify suitable predictors. The VAR model, optimized with Akaike's Information Criterion (AIC), was validated using the Mean Absolute Percentage Error (MAPE), which averaged 10.96% across forecasts for early 2023. Findings The analysis revealed that exchange rates (ER), domestic liquidity (M2), and GDP exert the strongest influence on construction material costs, whereas inflation rate (IR) and CPI showed weaker predictive value. The resulting CMCI captures material price dynamics more accurately than traditional univariate approaches, offering a replicable framework for emerging economies. Originality/value Unlike generic indices or black-box machine learning models, this study provides a transparent, statistically rigorous framework tailored to Egypt's construction sector. Its originality lies in (1) constructing a localized CMCI using the Laspeyres index method with a carefully selected base year, (2) integrating macroeconomic indicators into a multivariate VAR framework, and (3) validating predictive performance with real market data. This research therefore contributes a replicable, context-sensitive forecasting tool that can be adapted to other developing economies experiencing high material price volatility.

Pain patterns in patients with irritable bowel symptoms - A longitudinal observational time series approach.

Assessment of the Long-Term Impacts of Highway–Railway Grade Crossing Countermeasures: A Bayesian Vector Autoregression Modeling Approach

Impact of meteorological drivers on air quality index: A case study from Delhi.

This paper examines the time-frequency dynamics and the spillover effects among the clean energy, fossil fuel, metal, and electricity markets using a wavelet local multiple correlation analysis and a time-varying-parameter vector autoregression model. The findings suggest that the electricity market is isolated from the other markets in the short to medium term, while long-term interdependence is strong during crises such as the global financial crisis, the COVID-19 pandemic, and the Russia-Ukraine conflict. There exists a long-term integration trend across those four markets, with the fossil fuel and metal markets playing dominant roles. The fossil fuels market remained the primary channel through which systemic shocks were transmitted to all other sectors. The clean energy market has transformed from a market that passively absorbed shocks into a systemic driver during crises. These findings provide insights for investors and policymakers across different time horizons.

This study investigates the short-term contribution of agricultural exports to Tanzania’s economic growth from 1984 to 2023 using annual time series data and a Vector Autoregressive (VAR) model. The results reveal that agricultural exports are the most significant driver of GDP growth, with a 1% increase leading to a combined GDP growth of approximately 0.71% after periods two and three, indicating a delayed but substantial positive effect. Granger causality tests further confirm a unidirectional causal relationship from agricultural exports to economic growth, highlighting their key role in driving GDP. The labour force also positively influences growth, whereas gross capital formation exhibits a negative short-term impact. These findings align with the Export-Led Growth (ELG) theory, emphasising that exports stimulate foreign exchange earnings, investment, and productivity. Based on these results, the study recommends that the government prioritise policies promoting agricultural exports, including incentives for value addition, diversification of export crops, and improved access to international markets. Strengthening infrastructure, improving market access, providing technical support to farmers and agribusinesses, and facilitating trade through streamlined customs procedures can further enhance export competitiveness. Additionally, policies should focus on improving labour productivity through education, skills development, and inclusive participation in export-related activities. Implementing these measures is crucial for achieving the government’s target of raising agricultural export value to USD 5 billion by 2030.

Energy prices, including those of crude oil, natural gas, and electricity, are inherently volatile due to a wide range of influencing factors, such as geopolitical events, shifts in supply and demand, and fluctuations in weather conditions. These unpredictable movements pose challenges for decision-makers in energy-related industries, including policymakers, traders, and energy companies, all of whom require accurate forecasts to make informed choices. Predicting energy prices with a high degree of accuracy is essential for minimizing financial risks and ensuring stable supply and demand dynamics. This paper investigates the use of advanced statistical and machine learning models to forecast energy price movements more effectively. Specifically, we compare traditional time-series models, such as ARIMA (Autoregressive Integrated Moving Average), GARCH (Generalized Autoregressive Conditional Heteroskedasticity), and VAR (Vector Autoregressive), alongside hybrid models combining machine learning techniques. By integrating time-series characteristics, including seasonality, volatility clustering, and nonlinear behavior, we assess the effectiveness of each model in predicting price movements. The performance of the models is evaluated using standard accuracy metrics, including the Root Mean Squared Error (RMSE) and the Mean Absolute Percentage Error (MAPE), which allow us to compare forecast accuracy. Our findings reveal that hybrid ARIMA-GARCH-LSTM (Long Short-Term Memory) models significantly outperform traditional econometric approaches, excelling in both capturing the mean behavior and the volatility dynamics inherent in energy prices. This paper demonstrates that hybrid models offer superior forecasting capabilities by leveraging the strengths of both statistical and machine learning techniques, thus improving prediction accuracy for energy prices in volatile markets.

Background: In recent years, cryptocurrencies have emerged as alternative financial assets, gaining increasing attention in Nigeria due to rising inflation, currency depreciation, and restrictions on foreign exchange (FX) access. Aims: The study examined the effect of foreign exchange policies' spillover on the volatility of cryptocurrency returns in Nigeria. Methods: The study utilised time series data, which consisted of United States dollars and Nigerian Naira, while the cryptocurrencies were extracted from the Nigerian foreign exchange market over a two-year period. Vector Autoregressive (VAR) was employed as a method of data analysis. Results: The findings revealed that there is a statistically significant long-run relationship between FX rates and cryptocurrency returns. The exchange rate fluctuations, reflective of FX policy shifts, generate asymmetric and persistent volatility in major cryptocurrencies, and the impact of FX policies on crypto returns varies across assets, with BTC and ETH being more responsive than USDT and SOL. Conclusions: Cryptocurrencies can no longer be viewed in isolation, as they are increasingly influenced by global and national economic decisions. Implications: There is a need to incorporate macroeconomic indicators, particularly exchange rate trends, into trading strategies to anticipate market movements and reduce risk exposure.

Background: In recent years, cryptocurrencies have emerged as alternative financial assets, gaining increasing attention in Nigeria due to rising inflation, currency depreciation, and restrictions on foreign exchange (FX) access. Aims: The study examined the effect of foreign exchange policies' spillover on the volatility of cryptocurrency returns in Nigeria. Methods: The study utilised time series data, which consisted of United States dollars and Nigerian Naira, while the cryptocurrencies were extracted from the Nigerian foreign exchange market over a two-year period. Vector Autoregressive (VAR) was employed as a method of data analysis. Results: The findings revealed that there is a statistically significant long-run relationship between FX rates and cryptocurrency returns. The exchange rate fluctuations, reflective of FX policy shifts, generate asymmetric and persistent volatility in major cryptocurrencies, and the impact of FX policies on crypto returns varies across assets, with BTC and ETH being more responsive than USDT and SOL. Conclusions: Cryptocurrencies can no longer be viewed in isolation, as they are increasingly influenced by global and national economic decisions. Implications: There is a need to incorporate macroeconomic indicators, particularly exchange rate trends, into trading strategies to anticipate market movements and reduce risk exposure.

The existing body of literature encompasses the dimensions of institutional quality, as well as their relationships with various sectors, including foreign direct investment, trade, economic growth and the energy sector. However, a significant gap remains between institutional quality and geopolitical risk relationships. To investigate the association between institutional quality and geopolitical risk, we utilize panel vector autoregression and generalized method of moments on panel data for G20 countries from 2002 to 2022. Results from panel vector autoregression-generalized method of moments reveal that higher institutional quality, particularly political stability, plays a crucial mitigating role in reducing long-term geopolitical risk, indicating primarily unidirectional causality from institutions to geopolitical risk. However, geopolitical risk is persistent and self-reinforcing over time, with only limited and short-term effects on institutional indicators such as government effectiveness, political stability and rule of law, supporting weak reverse causality. Granger causality tests confirm limited bidirectionality, with institutional quality driving geopolitical risk reductions rather than vice versa.

ABSTRACT The study examines the dynamic effects of shocks in government spending and taxes on Polish economic activity. It does so by using the Baumeister and Hamilton method, applied to a three-equation structural VAR model. The results consistently indicate that positive government spending shocks have a positive impact on output, while positive tax shocks have a negative or insignificant impact. The impact spending multiplier is 1.25 and the impact tax multiplier is − 1.18 . In the long term the cumulative spending multiplier reaches 1.04 and is larger than the cumulative tax multiplier. The key elasticity of tax revenues to output is 2.33 .

To achieve accurate forecasting of future gold prices and thereby provide effective references for investors' decisions, this study selects time-series data of gold prices (gold), the U.S. Dollar Index (DXY), and WTI crude oil futures prices (WTI) from 2015 to 2020. Employing a comprehensive empirical framework that integrates stationarity tests, lag order selection, VAR model estimation, Wald tests, unit circle tests, Granger causality tests, impulse response analysis, and out-of-sample forecasting, this research systematically examines the dynamic interactive mechanisms among the three variables. The findings reveal a distinct asymmetric linkage effect: DXY exerts a more pronounced impact on gold prices than WTI, with a significant short-term negative correlation between first-order lagged DXY and gold. Granger causality tests confirm that DXY is a Granger cause of gold prices, while gold serves as a Granger cause for both DXY and WTI. Impulse response analysis further demonstrates that gold prices exhibit high short-term sensitivity to DXY shocks, with a negative inhibitory effect persisting for the first three periods before gradually weakening. Moreover, a 6-period ahead forecast of gold price first-order differences shows that the VAR model’s predicted values align closely with actual values in terms of trend and magnitude, verifying its reliability in forecasting. This study offers actionable insights for investors to mitigate risks and for policymakers to monitor the stability of the gold market.

Food prices are important global issue and their relationship with fuel prices has become a main concern in society. An increase in the subsidized fuel price on 3 September 2022 has allegedly caused a rise in food (grocery) prices. This paper conducts an empirical study to analyze the relationships between food prices in Indonesia: rice, chicken, beef, egg, red chili, cayenne, shallot, garlic, cooking oil, and sugar. The study uses time series data of food prices from 1 January 2018 to 31 December 2023, which consists of food prices from 87 traditional markets in Indonesia. The commodity prices are obtained from online public data provided by Bank Indonesia. It divides the analysis (pre- and post-3 September 2022) to see how the relationship between food prices changes due to the increase in the subsidized fuel price. It performs the Peter Clark (PC) algorithm to generate causal graphs from real datasets where the true graphs are unknown, complements the analysis by performing Vector Autoregression (VAR) to investigate the dynamic relationship between food prices, especially how the subsidized fuel price increase changes its dynamic relationship. The causal graphs from pre- and post-increasing fuel prices show the changes in the role of variable relationships, e.g., sugar and beef. The VAR results also show an interesting change in the IRF pattern. The results from both the PC algorithm and VAR show that there is a structural change in the relationship between food prices and that there is a different effect of price shock due to the subsidized fuel price increase. It might have been an indication of a change in the consumption pattern in society as a response to a food price increase. This must be a huge task to do in maintaining food prices when there is an adjustment in the subsidized fuel prices.

This study examines how three prominent forms of macro uncertainty—Economic Policy Uncertainty (EPU), Geopolitical Risk (GPR), and Oil Price Uncertainty (OPU)—affect the S&P 500 monthly log return (SP500return). A four-variable vector autoregression (VAR) is estimated after verifying stationarity in levels and selecting the lag order with information criteria. The empirical workflow includes unit-root tests, lag-length selection, system estimation, stability diagnostics based on characteristic roots, Granger causality tests, and orthogonalized impulse responses identified under a Cholesky ordering in the sequence EPU, GPR, OPU, SP500return. All series are I(0); information criteria favor VAR(4); and the system is covariance-stationary. Results show that EPU contains forward-looking predictive content for aggregate equity returns—higher EPU today is followed by lower SP500return. OPU delivers a short-run negative effect that mean-reverts by the mid-horizon, whereas GPR exhibits weak or statistically insignificant mean effects at the index level, consistent with transmission mainly through volatility and sectoral reallocation channels. Portfolio guidance follows: monitor EPU in macro-risk dashboards; during OPU upswings, trim exposures to cost- and rate-sensitive sectors and consider commodity or inflation-linked hedges; for GPR, prioritize volatility targeting and sector rotation over directional index positions. Future work can employ time-varying-parameter or regime-switching VARs, sign- or narrative-restricted SVARs, and local projections to capture state dependence and nonlinearities.

Bitcoin’s price dynamics are influenced by both internal factors (e.g., supply shocks, investor sentiment) and external drivers, among which the stability of stablecoins has attracted increasing academic and regulatory attention. This paper investigates the effect of stablecoin peg deviations (USDT and USDC) on Bitcoin returns using daily data from January 2020 to August 2025. Based on a vector autoregression (VAR) framework, we conduct unit root tests, lag order selection, model estimation, Granger causality tests, and impulse response analysis. Results show that both Bitcoin returns and stablecoin deviations exhibit strong short-term inertia. USDT and USDC deviations significantly Granger-cause Bitcoin returns, whereas the reverse causality is weaker. Impulse responses indicate that stablecoin deviations first produce positive shocks to Bitcoin returns, followed by negative corrections that gradually stabilize. The effect of USDT is more pronounced and persistent, underscoring its central role in cryptocurrency markets. These findings highlight the importance of monitoring stablecoin market stability, especially USDT, for investors and regulators seeking to manage systemic risks in crypto markets.

In the face of growing geopolitical instability, an important question remains whether ESG (Environmental, Social, and Corporate Governance) indices are sensitive to geopolitical shocks and whether they can act as protective assets. The aim of the study was to empirically compare the STOXX Global ESG Leaders index with the response of the technology sector (Nasdaq 100 and Philadelphia Semiconductor Index (SOX)) to changes in the geopolitical risk index (GPR). Monthly data from 2019 to 2025 were used, along with a procedure including Vector Autoregression (VAR) modeling, Impulse Response Function (IRF) analyses, the Johansen test, and Granger causality tests. The results indicate a lack of significant relationships between GPR and the analyzed indices in the short and long term: no cointegration was found, IRF responses were weak and quickly faded, and Granger tests did not demonstrate the predictive power of GPR for the analyzed markets. VAR forecasts additionally confirmed the stable trend, unrelated to GPR fluctuations. The results suggest that ESG indices are not directly affected by geopolitical shocks, which indicate their relative resilience. A similar response was observed for technological indices. The results may have practical implications for investors interested in sustainable investing while looking for stable assets in periods of global uncertainty. The results may be important for institutional investors in terms of portfolio stabilization functions during periods of increased geopolitical uncertainty, and for policymakers and market regulators in the context of designing frameworks supporting the stability of ESG markets.

In post-pandemic China, the expanding weight of services in overall economic activity and the stabilising role of official foreign exchange reserves (FER) elevate the informational value of high-frequency business surveys for interpreting equity market conditions. This study investigates the dynamic relations among China’s stock market index, the Manufacturing and Non-Manufacturing Purchasing Managers’ Index (PMI), and official FER using monthly data from April 2022 to July 2025. All series are log-transformed. Stationarity is assessed via Augmented Dickey-Fuller (ADF) tests to determine that all variables enter the model as first differences. A reduced-form vector autoregression (VAR) with a constant and four lags is estimated, followed by in-system Granger causality tests, orthogonalized impulse-response functions under a Cholesky scheme, and forecast-error variance decomposition (FEVD). Results indicate short-run mean reversion in the stock market index and limited immediate pass-through from manufacturing PMI and FER, while the non-manufacturing PMI exhibits the clearest medium-horizon influence. The FEVD results show that own shocks dominate at very short horizons, with services PMI gaining prominence over 6-12 months and reserves contributing moderately. However, manufacturing PMI accounts for the smallest share of the variance. Granger causality tests suggest that both PMI and FER impact the stock market index, while no causal relationship is found from the stock market index to these variables. These results highlight the role of services-sector signals for monitoring economic trends at medium horizons, with reserves primarily viewed as a stability indicator.

Geopolitical tensions can affect the stability of companies, including Indonesia's banking sector. Geopolitical risk, as measured by the Geopolitical Risk Index (GRI), is an external factor that reflects economic and political uncertainty arising from global adverse events. This study aims to understand the dynamics of the interaction of geopolitical risk on the stability of banking indicators, including ROA, ROE, CAR, and NPL. Furthermore, this study aims to assess financial resilience in the banking sector amid geopolitical tensions. Sampling was conducted across banking categories, including KBMI 4, KBMI 3, Islamic banks, and regional development banks. The data used are quarterly panel time series from 2015 to 2024. The analysis was conducted using Panel Vector Autoregression (PVAR) with the Generalised Method of Moments (GMM) approach. The results show that the impact of the GRI shock on banking indicators is short to medium-term, with the most significant effects on profitability (ROA, ROE) and credit quality (NPL). The results confirm that global geopolitical pressures play a dominant role in explaining fluctuations in bank indicators in Indonesia, particularly capital efficiency and capital structure. Based on the dynamics of these interactions, Bank A, Bank B, Bank D, and Bank E are classified as more resilient, and Bank C, Bank F, Bank G, and Bank H are classified as less resilient. More resilient banks are characterised by a low GRI impact on ROA and CAR, and a quick return to stability, while less resilient banks have a high GRI impact on ROE and CAR.

The endogenous money creation paradigm posits that banks generate money through lending, with deposits serving as a byproduct. This study investigates the mechanism driving the “credit–deposit paradox” during Poland’s high-interest-rate environment, introducing innovative methodological approaches to quantify systemic monetary impairment. Using comprehensive monthly data from 2006 to 2024, we employ a mixed-methods framework featuring: (1) Bayesian vector autoregression with Minnesota priors to test dynamic interdependencies; (2) a novel money shortage indicator (MSI) that operationalizes credit–deposit decoupling through three theoretically grounded components; (3) Markov regime-switching analysis to identify persistent monetary stress regimes. Key findings reveal a structural decoupling between deposit growth and credit creation, with robust evidence that exogenous money inflows accumulate as idle deposits rather than stimulating lending. The economy experienced significant periods of money shortage conditions, with the most severe impairment occurring during recent high-stress periods. The analysis confirms the dominance of cost-push inflation from energy and food prices, while monetary factors played a limited role. High interest rates amplified credit demand suppression, creating conditions consistent with endogenous money creation disruption. Methodologically, this study enables three key advances: (1) systematic measurement of monetary transmission breakdowns; (2) empirical identification of structural factors disrupting credit–deposit dynamics; (3) temporal characterization of monetary stress persistence patterns. These contributions advance the endogenous money framework by demonstrating its vulnerability to behavioral, policy-induced, and exogenous disruptions during high-stress periods. Practically, the MSI offers policymakers a real-time diagnostic tool for identifying monetary transmission breakdowns, while the regime analysis informs targeted countercyclical measures. Specific policy recommendations include developing sector-specific liquidity facilities, coordinating fiscal transfers with monetary policy to prevent deposit–loan decoupling, and prioritizing supply-side interventions during cost-push inflation episodes. By integrating post-Keynesian theory with empirical evidence from Poland, this study contributes to understanding money creation mechanisms in highly stressed economic environments.

This study examines the connectedness in returns and volatility between industrial and rare earth metals during the COVID-19 pandemic and the Russia–Ukraine conflict. For this purpose, we collected daily prices of six industrial and eight rare earth metals from June 16, 2014 to October 31, 2024, and employed the Time-Varying Parameter Vector Autoregression (TVP-VAR) approach. The findings reveal strong returns connectedness between industrial and rare earth metals in the COVID-19 pandemic and the Russia–Ukraine conflict, compared with the pre-COVID-19 pandemic. Moreover, among all metals, copper is the only metal that consistently transmits shocks in returns to the network of other metals, whereas DRM, CRM, nickel and lead consistently receive shocks in returns from other metals in the system. It implies that these four metals have observed most of the market shocks and act as safe-haven assets. It is important to note that BGM acts as a strong net transmitter during COVID-19 and acts as a strong net receiver in the Russia–Ukraine conflict. However, volatility connectedness is weak during the COVID-19 pandemic and the Russia–Ukraine conflict compared with pre-COVID-19 periods, implying that portfolio diversification opportunities exist in both crises. Furthermore, NDM is the only metal that acts as a net transmitter of volatility shocks, whereas zinc, tin, nickel, lead and copper act as net receivers of volatility shocks. These five metals absorbed most of the volatility shocks and act as a safe haven asset in the COVID-19 and the Russia–Ukraine conflict. The bivariate portfolio analysis shows that during COVID-19, the decrease in hedge ratios highlights the difficulty in establishing effective hedges. Interestingly, the increase in hedge ratios during the Russia–Ukraine conflict implies hedging opportunities. The findings of this study have important implications for investors and portfolio managers in managing portfolio risk through hedging and diversifying investment portfolios during periods of economic turmoil.