Search Results (47)

This study investigates the dynamic relationship between interest and noninterest income at Azer Turk Bank using quarterly data from 2016Q1–2024Q3. Unit root tests including Augmented Dickey–Fuller (ADF), Kwiatkowski–Phillips–Schmidt–Shin (KPSS), and Fourier–KPSS indicate that both variables are non-stationary in levels but become stationary after first differencing. The Hylleberg–Engle–Granger–Yoo (HEGY) test further shows that both series contain a unit root at the non-seasonal (0) frequency, while no unit roots are detected at the seasonal frequencies (π/2 and 3π/2). Johansen cointegration and the Fourier Autoregressive Distributed Lag (Fourier–ADL) framework confirm the existence of a stable long-run equilibrium. As a key methodological contribution, the study derives explicit Fourier-based Vector Error Correction Model (VECM) equations, enabling the modeling of cyclical deviations around nonlinear trends. Fourier Toda–Yamamoto and Breitung–Candelon frequency-domain causality tests reveal asymmetry: interest income consistently drives noninterest income in the short and medium run, whereas the reverse effect is weak. The results also confirm mean reversion, with deviations from equilibrium corrected within 5.9; 2.5 quarters. Overall, the findings highlight the limited diversification potential of noninterest income and the decisive role of lending in bank revenues, offering both methodological advances and practical guidance for macroprudential policy. Full article

This paper investigates the symmetric and asymmetric effects of renewable and non-renewable energy on Greece’s economic growth within an extended Cobb–Douglas production function for 1990–2022. The study is motivated by the rising role of renewable energy and the need to determine whether the energy–growth nexus is linear or nonlinear, an issue of central importance for policy. The Brock–Dechert–Scheinkman (BDS) test confirms the nonlinearity of the variables, while Zivot–Andrews unit root tests with structural breaks capture crisis-related disruptions. The Wald test indicates that renewable energy has an asymmetric long-run relationship with growth, whereas non-renewables exert symmetric effects. To model these dynamics, the Nonlinear Autoregressive Distributed Lag (NARDL) framework is applied. Results show that in the long run, positive shocks to renewable energy enhance growth, while both positive and negative shocks to non-renewables have symmetric impacts. In the short run, only non-renewable energy shocks significantly affect growth. Asymmetric causality analysis reveals a bidirectional relationship between positive renewable shocks and growth, suggesting a virtuous cycle of renewable expansion and economic performance. The study contributes by providing the first systematic evidence for Greece on the nonlinear energy–growth nexus, advancing empirical modeling with NARDL and break-adjusted tests, and highlighting the heterogeneous growth effects of renewable versus non-renewable energy. Full article

To address the challenges in predicting roof water hazards in weakly cemented strata of Northwest China, this study pioneers an integrated CNN-BiLSTM model for aquifer water abundance prediction. Focusing on the 1301E working face in the Yili No. 1 Coal Mine, we employed kriging interpolation to process sparse hydrological datasets (mean relative error: 8.7%), identifying five dominant controlling factors—aquifer burial depth, hydraulic conductivity, core recovery rate, sandstone–mudstone interbedded layer count, and sandstone equivalent thickness. The proposed bidirectional architecture synergizes CNN-based spatial feature extraction with BiLSTM-driven nonlinear temporal modeling, optimized via Bayesian algorithms to determine hyperparameters (32-channel convolutional kernels and 64-unit BiLSTM hidden layers). This framework achieves the comprehensive characterization of multifactorial synergistic effects. The experimental results demonstrate: (1) that the test set root mean square error (1.57 × 10⁻³) shows 65.3% and 85.9% reductions compared to the GA-BP and standalone CNN models, respectively; (2) that the coefficient of determination (R² = 0.9966) significantly outperforms the conventional fuzzy analytic hierarchy process (FAHP, error: 0.071 L/(s·m)) and BP-based neural networks; (3) that water abundance zoning reveals predominantly weak water-rich zones (q = 0.05–0.1 L/(s·m)), with 93.3% spatial consistency between predictions and pumping test data. Full article

This paper investigates whether trade frictions, in the form of exchange controls, are among the main obstacles preventing the Purchasing Power Parity (PPP) hypothesis from being valid among trading nations. It specifically looks at whether exchange controls—a type of trade friction—hinder PPP’s applicability in the relationship between an emerging economy, South Africa, and its major trading partners, classified by their use of exchange control regulations. The methodology used to test the PPP hypothesis includes nonlinearity through quantile unit root tests and quantile cointegration, designed to capture the varied economic conditions across trading nations. The empirical findings indicate that trade frictions may not necessarily obstruct the validity of the PPP hypothesis. Moreover, the weak form of the PPP hypothesis predominantly appears at the extreme quantiles of the real exchange rate among trading nations, especially the lower quantile, which is associated with the real exchange rate depreciation of the South African economy. This insight is significant for both policymakers and investors. Full article

Porosity prediction from seismic data is of significance in reservoir property assessment, reservoir architecture delineation, and reservoir model building. However, it is still challenging to use traditional model-driven methodology to characterize carbonate reservoirs because of the highly nonlinear mapping relationship between porosity and elastic properties. To address this issue, this study proposes an advanced spatiotemporal deep learning neural network for porosity prediction, which uses the convolutional neural network (CNN) structure to extract spatial characteristics and the bidirectional gated recurrent unit (BiGRU) network to gather temporal characteristics, guaranteeing that the model accurately captures the spatiotemporal features of well logs and seismic data. This method involves selecting sensitive elastic parameters as inputs, standardizing multiple sample sets, training the spatiotemporal network using logging data, and applying the trained model to seismic elastic attributes. In blind well tests, the CNN–BiGRU model achieves a 54% reduction in the root mean square error and a 6% correlation coefficient improvement, outperforming the baseline models and traditional nonlinear fitting (NLF). The application of the proposed method to seismic data indicates that the model yields a reasonable porosity distribution for tight carbonate reservoirs, proving the strong generalization ability of the proposed model. This method compensates for the limitations of individual deep learning models by simultaneously capturing the spatial and temporal components of data and improving the estimation accuracy, showing considerable promise for accurate reservoir parameter estimation. Full article

The study on the link concerning FDI and environmental emissions has been the interest in recent environmental economics subject. The interest of this research work is to dynamically understand the effect of FDI on environmental emissions in South Africa. The research applied the renowned Markov-switching regression to explore the association among the variables. Prior to the formal estimation, the data were subjected to a linearity test, non-linear unit root test and cusum test so to ascertain whether the variables conform to non-linearity modeling. The results demonstrated that in both regimes (lower or higher emissions), the influence of FDI is positive and statistically significant. This finding implies that foreign investment is detrimental to our environment, irrespective of regime changes. This finding supports the Pollution Haven Hypothesis (PHH). Furthermore, the results show that emissions in South Africa stay in a low or high regime for a short period between one and two years. Policy implications to the results are that economic and climate change policy makers in South Africa should start to regulate FDI to be environmentally friendly. Full article

This paper examines the sustainability of the current account balances for three leading economies that play significant roles in global financial and geopolitical developments: Using the concept of sustainability as the ability of an economy to meet its long-term intertemporal budget constraint, the analysis evaluates whether the current accounts of these three economies can maintain this condition without requiring substantial policy interventions or significant changes in private sector behavior. The stationarity of the current account is considered a sufficient condition for testing the sustainability of the current account balance. However, it is argued that the common assumption of a linear process for the current account under the alternative hypothesis of stationarity may not accurately reflect reality, as the current account may exhibit non-linear behavior. Therefore, both linear and non-linear unit root tests are employed to investigate current account sustainability. The results of the unit root tests indicate that the current account balances of Germany, China, and the United States are unsustainable. These findings have significant implications for the economic stability and policy direction of these major economies. Full article

Accurately predicting oil well production volume is of great significance in oilfield production. To overcome the shortcomings in the current study of oil well production prediction, we propose a hybrid model (GRU-KAN) with the gated recurrent unit (GRU) and Kolmogorov–Arnold network (KAN). The GRU-KAN model utilizes GRU to extract temporal features and KAN to capture complex nonlinear relationships. First, the MissForest algorithm is employed to handle anomalous data, improving data quality. The Pearson correlation coefficient is used to select the most significant features. These selected features are used as input to the GRU-KAN model to establish the oil well production prediction model. Then, the Particle Swarm Optimization (PSO) algorithm is used to enhance the predictive performance. Finally, the model is evaluated on the test set. The validity of the model was verified on two oil wells and the results on well F14 show that the proposed GRU-KAN model achieves a Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination ($R^2$) values of 11.90, 9.18, 6.0% and 0.95, respectively. Compared to popular single and hybrid models, the GRU-KAN model achieves higher production-prediction accuracy and higher computational efficiency. The model can be applied to the formulation of oilfield-development plans, which is of great theoretical and practical significance to the advancement of oilfield technology levels. Full article

To reduce the disasters caused by slope instability, this paper proposes a new machine learning (ML) model for slope stability prediction. This improved SVR model uses support vector machine regression (SVR) as the basic prediction tool and the grid search method with 5-fold cross-validation to optimize the hyperparameters to improve the prediction performance. Six features, namely, unit weight, cohesion, friction angle, slope angle, slope height, and pore pressure ratio, were taken as the input of the model, and the factor of safety was taken as the model output. Four statistical indicators, namely, the coefficient of determination (R²), mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE), were introduced to assess the generalization performance of the model. Finally, the feature importance score of the features was clarified by calculating the importance of the six features and visualizing them. The results show that the model can well describe the nonlinear relationship between features and the factor of safety. The R², MAPE, MAE, and RMSE of the testing dataset were 0.901, 7.41%, 0.082, and 0.133, respectively. Compared with other ML models, the improved SVR model had a better effect. The most sensitive feature was unit weight. Full article

Energy sources, one of the key elements of economic growth and development, have recently come to the forefront in terms of sustainability, security of supply, low cost, and environmental impact. Therefore, the diversification of energy sources is becoming more important; in this regard many countries are investing especially in renewable energy sources. This trend plays an important role in the decarbonization of the energy sector. The aim of this study is to analyze the convergence of electricity capacity in G20 countries, which account for two-thirds of the world population and have a dominant position in the world economy. Accordingly, the analysis was carried out for total electricity capacity and its sources (nuclear, fossil fuels, and renewables). Unlike other studies in the literature, this study utilizes nonlinear unit root tests with Fourier function, which models nonlinearity and structural break, the two main problems in unit root tests, within the framework of recent developments in time series analysis. According to the findings of the analysis, it was concluded that the converging countries are in line with the G20 policies in terms of electricity capacity and its sources and that there is no need for policy changes in these countries. Full article

The sustainability of the current account balance for five fragile economies—Brazil, Argentina, South Africa, India, and Türkiye (namely, BASIT)—is investigated. These countries’ economies operate under a time current account deficit almost all the time, a condition that causes fragility to external shocks; the following fallout from these shocks may risk not only the domestic economy but also the international economy, such as by clogging trade and income distribution. In this study, the sustainability of the current account in BASIT countries is examined via wavelet-based Kapetanios, Shin and Snell (WKSS) and Fourier wavelet-based KSS (FWKSS) unit root tests, in conjunction with linear unit root tests. Even though traditional unit root tests generally support the sustainability of a current account deficit for all countries, a non-linear unit roots test confirms the traditional tests for only India and South Africa. Results from the wavelet transform of non-linear unit root tests indicate the unsustainability of the current account balance, except in the case Türkiye. Moreover, the FWKSS test confirms WKSS. Full article

The stock market, characterised by its complexity and dynamic nature, presents significant challenges for predictive analytics. This research compares the effectiveness of neural network models in predicting the S&P500 index, recognising that a critical component of financial decision making is market volatility. The research examines neural network models such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and Gated Recurrent Unit (GRU), taking into account their individual characteristics of pattern recognition, sequential data processing, and handling of nonlinear relationships. These models are analysed using key performance indicators such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy, a metric considered essential for prediction in both the training and testing phases of this research. The results show that although each model has its own advantages, the GRU and CNN models perform particularly well according to these metrics. GRU has the lowest error metrics, indicating its robustness in accurate prediction, while CNN has the highest directional accuracy in testing, indicating its efficiency in data processing. This study highlights the potential of combining metrics for neural network models for consideration when making decisions due to the changing dynamics of the stock market. Full article

This study investigates the asymmetric effects of economic policy uncertainty (EPU) on food security in Nigeria, utilizing annual time series data from 1970 to 2021. The study used descriptive statistics, unit root tests, the nonlinear autoregressive distributed lag (NARDL) model and its associated Bounds tests to analyze the data. The analysis reveals that adult population, environmental degradation, exchange rate uncertainty (EXRU), financial deepening, food security (FS), government expenditure in agriculture uncertainty (GEAU), inflation, and interest rate uncertainty (INRU) exhibit positive mean values over the period, with varying degrees of volatility. Cointegration tests indicate a long-term relationship between EPU variables (GEAU, INRU, and EXRU) and food security. The study finds that cumulative positive and negative EPU variables have significant effects on food security in the short run. Specifically, negative GEAU, positive INRU, positive and negative EXRU have significant effects in the short run. In the long run, negative GEAU, positive and negative EXRU have significant effects on food security. Additionally, the research highlights asymmetric effects, showing that the influence of GEAU and EXRU on food security differs in the short- and long-run. The study underscores the importance of increased government expenditure on agriculture, control of exchange rate and interest rate uncertainty, and the reduction in economic policy uncertainty to mitigate risks in the agricultural sector and enhance food security. Recommendations include strategies to stabilize exchange rates to safeguard food supply and overall food security. Full article

This paper augments the sparse literature on the inflationary impact of foreign debt in Brazil while addressing methodological caveats in previous studies. We depart from the linearity assumption and employ two nonlinear techniques: the nonlinear autoregressive distributed lag (NARDL) model and a Markov Switching Regression (MSR) to investigate the connection between foreign debt and inflation within a multivariate framework. The analyses consider the presence of structural breaks via assessing variable stationarity using the Zivot and Andrew unit root test and incorporating a residual-based cointegration test proposed by Gregory and Hansen. Additionally, we apply a multiple structural breakpoints test by Bai and Perron to determine the presence of structural breaks in the impact of foreign debt on inflation. Our findings robustly indicate that the domestic money supply has a statistically significant positive effect, while the nominal effective exchange rate has a negative effect on inflation in both the short and long run. The NARDL model reveals that only positive changes in foreign debt have a statistically significant negative effect on inflation in the short run, whereas both positive and negative foreign debt changes significantly affect inflation in the long run. The results from the MSR model are generally consistent with those of the NARDL model. Full article

This paper examines the extent to which uncertainty in the energy market, the financial market, the commodity market, the economic policy, and the geopolitical events affect crude oil returns. To consider the complex properties of time series, such as nonlinearity, temporal variability, and unit roots, we adopt a two-instrument technique in the time–frequency domain that employs the DCC-GARCH (1.1) model and the Granger causality test in the frequency domain. This allows us to estimate the dynamic transmission of uncertainty from various sources to the oil market in the time and frequency domains. Significant dynamic conditional correlations over time are found between oil returns—commodity uncertainty, oil returns—equity market uncertainty, and oil returns—energy uncertainty. Furthermore, at each frequency, the empirical results demonstrate a significant spillover effect from the commodity, energy, and financial markets to the oil market. Additionally, we discover that sources with high persistence volatility (such as commodities, energy, and financial markets) have more interactions with the oil market than sources with low persistence volatility (economic policy and geopolitical risk events). Our findings have significant ramifications for boosting investor trust in risky energy assets. Full article