Nonlinear Autoregressive Research Articles

Analyzing the Impact of Vision 2030’s Economic Reforms on Saudi Arabia’s Consumer Price Index

This study examines the relationship between CO2 emissions, labor force participation, foreign direct investment (FDI), and trade openness on the Consumer Price Index (CPI) in Saudi Arabia, within the context of Vision 2030’s economic reforms. Vision 2030 aims to diversify the economy, reduce oil dependency, and promote sustainable growth, making it crucial to understand the factors influencing inflation and economic stability. Using annual data from 2001 to 2022 and the nonlinear Autoregressive Distributed Lag (NARDL) bounds testing approach, the study analyzes both short- and long-term effects. The findings reveal that higher CO2 emissions have a deflationary effect, reducing the CPI in both the short and long term, while FDI shows an inflationary impact with a delayed effect. Labor force expansion contributes to lowering the CPI, reflecting its deflationary pressure, especially over the long term. Trade openness is also examined for its dual effects on CPI, In the short run, both positive and negative trade openness reduce consumer prices, while in the long run, positive trade openness increases inflation, and negative trade openness lowers prices. This shows the differing inflationary impacts of trade openness over time. These findings contribute to the policy discourse on balancing economic growth, environmental sustainability, and inflation management, offering strategic insights for policymakers in alignment with Saudi Arabia’s Vision 2030 objectives.

Asymmetric impact of the COVID-19 on the Moroccan stock exchange

PurposeThis study aims to investigate the asymmetric impact of the COVID-19 pandemic on the stock market returns of companies listed on the Casablanca Stock Exchange.Design/methodology/approachTo achieve this objective, we utilized the SymCovid series, which comprises two sub-series: one representing the deterioration of the pandemic situation (NEG) and the other representing the improvement of the pandemic situation (POS). We employed the Nonlinear Autoregressive Distributed Lag model, incorporating Bounds Testing as proposed by Pesaran and Shin (1999) and Pesaran et al. (2001), to explore the asymmetry of the pandemic’s impact on stock prices listed on the Casablanca stock exchange.FindingsOur analysis using the NARDL econometric model reveals an asymmetric effect of COVID-19 on stock prices. Notably, we observe that stock prices react more strongly to a worsening pandemic situation than to an improvement, on average. Furthermore, our main findings indicate that while the improvement in the pandemic situation has no significant long-term impact on stock prices, it does exhibit a significant positive effect in the short term. Conversely, the deterioration in the pandemic situation has a more pronounced negative effect on stock prices in the long term than the short term.Originality/valueOur study fills a gap in the existing literature by focusing on the impact of the COVID-19 pandemic on stock returns in the context of the Casablanca Stock Exchange, which has been relatively understudied compared to other regions such as Asia, Europe and the Americas. We go beyond previous research by examining whether stock returns exhibit asymmetric responses to changes in the pandemic situation, highlighting potentially unique dynamics in emerging market economies during crises and providing valuable information for investors, policymakers and researchers.

Defence Spending and Income Inequality. An Asymmetric Empirical Evidence from a Decomposition Analysis of Defence Spending in the Case of Greece

ABSTRACT This study is the first to empirically examine the asymmetric and compositional effects of defence spending on three different measures of income disparity. Using the Nonlinear Autoregressive Distributed Lag (NARDL) approach, the study analyses the short- and long-term impacts of disaggregated defence spending on income inequality in Greece. The findings have significant theoretical and policy implications. First, in the medium term, increased military spending in Greece widens income disparity by boosting the income share of the wealthiest. Second, in the long term, positive changes in military expenditures reduce income inequality, primarily benefiting lower-income groups. Third, long-term negative changes in defence spending harm middle- and low-income groups. Finally, the compositional analysis highlights that increases in military spending, mostly on personnel and, to a lesser extent, on equipment, improve long-term income equality, while decreases in spending on personnel significantly worsen income disparity. Given these findings and the evolving nature of warfare, Greece should gradually adopt a new defence strategy. This strategy should emphasise extensive personnel reserves, prioritise domestic military equipment over costly foreign alternatives, and rationalise defence expenditures by reallocating budget expenditures towards health, education, and social welfare programs. These measures would enhance living standards and reduce income inequality in Greece.

Inverse algorithm for boundary heat flux density based on the NARX neural network

Abstract The inverse heat transfer problem is vital for scientific research and engineering applications. This paper introduces a method using the Nonlinear Autoregressive with Exogenous Inputs (NARX) neural network to identify heat boundary conditions in nonlinear transient heat transfer processes in real time. This method has two notable advantages: (1) It relies solely on surface temperature time series to obtain inversion results; (2) Even in the absence of knowledge regarding the system’s state equations, it can estimate heat flux density. The NARX neural network is trained by using Bayesian regularization with surface temperature and heat flux data. (3) As per the inversion results, the NARX neural network’s accuracy in predicting the boundary heat flux density (BHFD) increases as the temperature measurement points approach the heat flux boundary. This neural network calculates the current heat flux density by incorporating both present and past surface temperature measurements as inputs. Through numerical simulation experiments, the efficacy of the NARX method is confirmed, showcasing its exceptional accuracy, robustness against noise, and broad suitability.

Comparative Evaluation of Water Level Forecasting Using IoT Sensor Data: Hydrodynamic Model SWMM vs. Machine Learning Models Based on NARX Framework

This study evaluates the accuracy of water level forecasting using two approaches: the hydrodynamic model SWMM and machine learning (ML) models based on the Nonlinear Autoregressive with Exogenous Inputs (NARX) framework. SWMM offers a physically based modeling approach, while NARX is a data-driven method. Both models use real-time precipitation data, with their predictions compared against measurements from a network of IoT sensors in a stormwater management system. The results demonstrate that while both models provide effective forecasts, NARX models exhibit higher accuracy, with improved Nash–Sutcliffe Efficiency (NSE) coefficients and 33–37% lower mean absolute error (MAE) compared to SWMM. Despite these advantages, NARX models may struggle with limited data on extreme flooding events, where they could face accuracy challenges. Enhancements in SWMM modeling and calibration could reduce the performance gap, but the development of SWMM models requires substantial expertise and resources. In contrast, NARX models are generally more resource-efficient. Future research should focus on integrating both approaches by leveraging SWMM simulations to generate synthetic data, particularly for extreme weather events, to enhance the robustness of NARX and other ML models in real-world flood prediction scenarios.

A Novel Model for Prediction of Flashover 150kV Polluted Insulator Based on Nonlinear Autoregressive External Input Neural Network

A Novel Model for Prediction of Flashover 150kV Polluted Insulator Based on Nonlinear Autoregressive External Input Neural Network

The Asymmetric Effects of Oil Prices on Stock Returns: Evidence from Hanoi Stock Exchange, Vietnam

This study is devoted to investigating the asymmetric effects of oil prices on stock returns for Hanoi Stock Exchange (HNX). The data used in this study are weekly series of HNX-Index, WTI crude oil prices and geopolitical risks (GPRs) Index covering the period from January 2, 2010 to December 31, 2023. This study employed a nonlinear Autoregressive Distributed Lag (NARDL) bounds testing approach to estimate the short-term and long-term asymmetric effects of oil prices on the market returns. We found that in the short-term, oil prices have negative asymmetric effects on the market returns. Specifically, one percent increase in positive changes of oil prices immediately leads to 0.0085 percent decrease in the market returns. However, one percent increase in negative changes of oil prices is associated with 0.1487 percent decrease in the market returns. In the long-term, the estimated results confirm that both the negative and positive changes of oil prices have significantly negative effects on the market returns. Finally, the results obtained from the error correction model (ECM) indicate that 81.54 percent of the disequilibria from the previous week are converged and corrected back to the long-term equilibrium in the current week.

Harmonizing sustainable Horizons: Deciphering the intricate nexus between disaggregated financial inclusion and clean energy transition in BRICS economies

This study emphasizes the acceleration of clean energy (CE) adoption through financial inclusion (FI), particularly in the context of the rapid expansion of the BRICS economic cooperation alliance and its contribution to addressing environmental challenges. Specifically, this research assesses the impact of FI on the upward mobility of clean energy transition using annual panel data from 2004 to 2022. Utilizing a comprehensive set of pre-estimation techniques, this study employs the newly established Panel non-linear Autoregressive Distributed Lag (P-NARDL) model for empirical analysis, supplemented by robust estimation methods. The findings reveal that various dimensions of FI, including financial accessibility, financial depth, financial efficiency, and financial stability, significantly influence the progression from nonrenewable energy sources to clean energy consumption. This study extends the examination to assess both the aggregate and disaggregated relationships among the variables. Using causality estimation, it identifies the presence of both bidirectional and unidirectional causality between the FI indices and CE. These findings offer practical policy implications for developing financial resources to accelerate the adoption of CE, thereby addressing the environmental challenges faced by the BRICS economies.

Public debt dynamics in Brazil through the lens of its conditioning factors

ABSTRACT This article aims to investigate the short and long-run behaviour of the Brazilian public debt, through the lens of the conditioning factors, from December/2006 to September/2022. The econometric approach consists of estimating Nonlinear Autoregressive Distributed Lag Models (NARDL) – Bounds Testing Approach to Cointegration. The main empirical results show: i) a long-term relationship (cointegration) between net and gross debt and their respective conditioning factors; ii) higher speed of adjustment to long-run equilibrium for the gross debt (20%), due to exchange rate adjustments and its asset-only approach; iii) slow net debt adjustment due to its asset-liability consolidation; iv) accumulation foreign reserves closely related to the response given by the gross debt, when affected by exchange rate shocks; iv) the search for a balance in public accounts seems to be a good strategy, as shown by the relationship between primary results/net issuance and net/gross debt; v) an economic activity expansion helps control public debt in Brazil, with a greater sensitivity of the gross debt, compared to the net debt; vi) the role played by the Brazilian Central Bank, together with the accumulation of foreign reserves, is crucial to understanding the recent differences in the trajectories of net and gross debt in Brazil.

Towards sustainable development: Examining renewable energy consumption in E−7 countries

In the contemporary landscape, sustainable development became major challenge for the economy which is tackled if environmental issues are resolved. In this regard, this study investigate renewable energy, institutional quality, foreign direct investment (FDI), economic growth on environmental pollution in E−7 countries (Brazil, Russia, China, Indonesia, India, Mexico, and Turkey). Utilizing annual data from 2002 to 2023, selected the panel Nonlinear Autoregressive Distributed Lag (NARDL) after applying stationary process. The results depict that there is short and long run relationship among the selected variables, CO2 emissions exhibit an asymmetrical response to positive and negative shocks in exogenous variables. A one-percent change in FDI, GDP, IQ and EC reduce the carbon emission. Our research concludes with policy recommendation that prioritize priorities economic growth and environmental sustainability. To achieve a healthy environment and sustainable growth in the E7 countries, it is essential to strengthen institutional frameworks, encourage green investments, and foster technical innovation.

A novel stochastic ProFiVaS model based on decomposition of stochastic Vasicek differential equation for modeling and simulating of financial indicators

The main purpose of this study is to develop a new discrete time stochastic ProFiVaS model based on decomposition of stochastic Vasicek differential equation which follows bouncing autoregressive AR1, with fixed variance and trend for modeling and simulating of international financial time series forecasting.This study offers the four discrete time stochastic models. The first model is determined as autoregressive integrated moving average-Arima1,1,1, and the parameter estimates of this model are found using the maximum likelihood method (MLM). Monte Carlo simulation is applied to the obtained parameters. This model is named MoCArima for short. Second, the MoCArima model whose parameters are estimated with particle swarm optimization (PSO) is determined as Proposed Monte Carlo (MC) Arima1,1,1, and is briefly named as ProMoCArima. The third model is determined as Fixed Variance Stochastic Model which has a bouncing autoregressive AR1 structure with constant variance and trend component. This model is briefly named as FiVaS. The parameter estimates are found using the MLM. The fourth model is determined as the Proposed Fixed Variance Stochastic Model. This model is briefly named as ProFiVaS. Structurally the same as the third model. The parameter estimates of both trend and stochastic parts of the FiVaS model are found by the PSO.Proposed models are applied to forecasting international different financial indicators to show its superiority and applicability. The performance and usage efficiency of the proposed four stochastic models are examined on a total of six different international financial data types, five of which are stock index data BIST100 (Türkiye), S&P500 (USA), DAX (Germany), FTSE100 (UK), NIKKEI225 (Japan), and one of which is the exchange rate EURO/USD. The ProFiVaS among all models provides the best performance on the all indices. The ProFiVaS can easily be extended to any real-world time series prediction requirements.The proposed models can be extended to all stationary and non-stationary linear or nonlinear autoregression models opening the door to modeling data types obtained from different sector fields. A part of the future plan of this work is to design a decision support system software that assigns a value of 1 when an investor needs to invest in a financial indicator, and 0 otherwise.

Exchange rate movements and trade balance in developing economies: Evidence of symmetry and country-specific asymmetries

The impact of exchange rate (ER) movements on the trade balance has been extensively debated in the literature, with conflicting views on the nature and magnitude of this effect. This paper investigates the relationship between ER movements and trade balance in developing economies, with a specific focus on bilateral analysis involving Pakistan. Employing the Nonlinear Autoregressive Distributed Lag (NARDL) model, our results reveal a symmetric relationship between positive and negative ER changes and the trade balance, showing significant long-term and short-term effects across the panel of developing economies. However, country-specific asymmetries are observed, especially in the cases of India and China, and are more pronounced in each bilateral analysis, whether in the short run or long run. The study findings highlight the critical importance of maintaining a stable exchange rate policy, which can provide policymakers with precise insights into the money market and the broader economy while preventing high volatility and continuous depreciation of domestic currencies. Such measures can mitigate the long-term adverse effects of ER movements on trade agreements. The study offers valuable insights for policymakers and investors in developing economies, aiding them in making informed decisions about exchange rate policies.

Military Outlays and Economic Growth: A Nonlinear Disaggregated Analysis for a Developed Economy

Abstract There is a dearth of comprehensive studies examining the compositional and asymmetric effects of defence spending on simultaneous economic growth. This study uses the Nonlinear Autoregressive Distributed Lag (NARDL) approach to analyse how disaggregated defence spending affects economic growth in Greece in the short and long term. The results hold significant theoretical and practical policy implications. First, military spending boosts economic growth in the short term but hampers it in the long run. Second, the long-term effects of positive and negative defence spending shocks are distinct, with positive shocks more detrimental to economic growth than the benefits of negative shocks. Finally, our study reveals that personnel expenditures have the most significant and enduring effects on economic growth compared to other military spending categories. Based on these results, Greece should adopt a new defence doctrine that relies on extensive personnel reserves, prioritises state intelligence and production technology, and promotes domestic military equipment over expensive foreign options.

The nonlinear impacts of macroeconomic shocks on the US regional housing markets

ABSTRACT This paper examines the asymmetrical impacts of aggregate macroeconomic shocks on the US regional housing markets by estimating nonlinear Vector Autoregressive models. We show how aggregate output, monetary, inflation, and credit shocks result in heterogenous impulse responses of the housing market volatilities across growing and declining housing market regimes in nine census divisions and 19 metropolitan cities. We find that output and inflation shocks cause qualitatively asymmetrical responses across regimes in majority of regions. A positive output shock and a negative inflation shock reduce (increase) housing price uncertainties when housing markets are in a declining (booming) regime in most regions. The responses of regional housing price volatilities to monetary and credit shocks are quantitatively asymmetrical across regimes in most regions. We also find that aggregate shocks have larger contributions on changing the probabilities of regime switching when a regional housing market is initially expanding than declining. Among aggregate shocks, inflation shock has the most prominent contributions across most regions. It also presents the strongest asymmetrical effects across positive and negative shocks. Our results can help regional decision makers utilize fiscal policies, regulatory measures, and regional planning to mitigate the destabilizing effects of macroeconomic shocks on regional housing markets.

High-percentage new energy distribution network line loss frequency division prediction based on wavelet transform and BIGRU-LSTM.

The access of new energy improves the flexibility of distribution network operation, but also leads to more complex mechanism of line loss. Therefore, starting from the nonlinear, fluctuating and multi-scale characteristics of line loss data, and based on the idea of decomposition prediction, this paper proposes a new method of line loss frequency division prediction based on wavelet transform and BIGRU-LSTM (Bidirectional Gated Recurrent Unit-Long Short Term Memory Network).Firstly, the grey relation analysis and the improved NARMA (Nonlinear Autoregressive Moving Average) correlation analysis method are used to extract the non-temporal and temporal influencing factors of line loss, and the corresponding feature data set is constructed. Then, the historical line loss data is decomposed into physical signals of different frequency bands by using wavelet transform, and the multi-dimensional input data of the prediction network is formed with the above characteristic data set. Finally, the BIGRU-LSTM prediction network is built to realize the probabilistic prediction of high-frequency and low-frequency components of line loss. The effectiveness and applicability of the method proposed in this paper were verified through numerical simulation. By dividing the line loss data into different frequency bands for frequency prediction, the mapping relationship between different line loss components and influencing factors was accurately matched, thereby improving the prediction accuracy.

Elevating hourly PM2.5 forecasting in Istanbul, Türkiye: Leveraging ERA5 reanalysis and genetic algorithms in a comparative machine learning model analysis

Rapid urbanization and industrialization have intensified air pollution, posing severe health risks and necessitating accurate PM2.5 predictions for effective urban air quality management. This study distinguishes itself by utilizing high-resolution ERA5 reanalysis data for a grid-based spatial analysis of Istanbul, Türkiye, a densely populated city with diverse pollutant sources. It assesses the predictive accuracy of advanced machine learning (ML) models—Multiple Linear Regression (MLR), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGB), Random Forest (RF), and Nonlinear Autoregressive with Exogenous Inputs (NARX). Notably, it introduces genetic algorithm optimization for the NARX model to enhance its performance. The models were trained on hourly PM2.5 concentrations from twenty monitoring stations across 2020–2021. Istanbul was divided into seven regions based on ERA5 grid distributions to examine PM2.5 spatial variability. Seventeen input variables from ERA5, including meteorological, land cover, and vegetation parameters, were analyzed using the Neighborhood Component Analysis (NCA) method to identify the most predictive variables. Comparative analysis showed that while all models provided valuable insights (RF > LGB > XGB > MLR), the NARX model outperformed them, particularly with the complex dataset used. The NARX model achieved a high R-value (0.89), low RMSE (5.24 μg/m³), and low MAE (2.94 μg/m³). It performed best in autumn and winter, with the highest accuracy in Region-1 (R-value 0.94) and the lowest in Region-5 (R-value 0.75). This study's success in a complex urban setting with limited monitoring underscores the robustness of the NARX model and the methodology's potential for global application in similar urban contexts. By addressing temporal and spatial variability in air quality predictions, this research sets a new benchmark and highlights the importance of advanced data analysis techniques for developing targeted pollution control strategies and public health policies.

An Enhanced Forecasting Method of Daily Solar Irradiance in Southwestern France: A Hybrid Nonlinear Autoregressive with Exogenous Inputs with Long Short-Term Memory Approach

The increasing global reliance on renewable energy sources, particularly solar energy, underscores the critical importance of accurate solar irradiance forecasting. As solar capacity continues to grow, precise predictions of solar irradiance become essential for optimizing the performance and reliability of photovoltaic (PV) systems. This study introduces a novel hybrid forecasting model that integrates Nonlinear Autoregressive with Exogenous Inputs (NARX) with Long Short-Term Memory (LSTM) networks. The purpose is to enhance the precision of predicting daily solar irradiance in fluctuating meteorological scenarios, particularly in southwestern France. The hybrid model employs the NARX model’s capacity to handle complex non-linear relationships and the LSTM’s aptitude to manage long-term dependencies in time-series data. The performance metrics of the hybrid NARX-LSTM model were thoroughly assessed, revealing a mean absolute error (MAE) of 9.58 W/m2, a root mean square error (RMSE) of 16.30 W/m2, and a Coefficient of Determination (R2) of 0.997. Consequently, the proposed hybrid model outperforms the benchmark model in all metrics, showing a significant improvement in prediction accuracy and better alignment with the observed data. These results highlight the model’s effectiveness in enhancing forecasting accuracy under unpredictable conditions, improving solar energy integration into power systems, and ensuring more reliable energy predictions.

Analysis of the Housing Market Dynamics Using NARX Neural Network

This study employs a Nonlinear Autoregressive with eXogenous inputs (NARX) neural network to model the dynamics of the housing construction market in Poland, with a distinction made between segments of developers and individual investors. The dataset under analysis contains the 19-year data corresponding to the numbers of housing units approved for construction, under construction, and completed. The NARX model was calibrated thoroughly to suit unique characteristics of the data, with an emphasis put on the hidden layer size and delay parameters, to capture the estate market's nonlinear trends. Results show a very high efficiency of NARX models and highlight distinct patterns and dynamics in the housing completion, construction starts, and permit issuance between the two market segments. These variations are vital for understanding the distinct forces and trends shaping the developers’ and individual investors’ markets in the Polish housing sector. Findings of the analysis provide valuable insight into the nanced functioning of these market segments.

Prediction models to control final control elements for neutralization process in chemical industries

AbstractBACKGROUNDIndustrialization continues to increase in the 21st century, as industries start moving towards automation. Manufacturing of a component/item requires a lot of resources and energy. Usage of water in industries has increased in recent years, as it is used as both a resource and an energy generating source. Neutralization of a processed liquid is carried out by addition of solution through final control elements and most industries are utilizing pneumatic‐based control valves, which have a disadvantage in the regulation of additives.RESULTSA motorized valve system is introduced, which is compared with the pneumatic system in terms of their performance. The proposed system is implemented with different controllers such as proportional–integral controller tuned using linear matrix inequalities, modified internal model controller, model predictive controller and nonlinear autoregressive (NAR) model, where the final control elements are tested for pH neutralization. On implementation of the proposed system the NAR model has proven to be better under the motorized control valve system for the neutralization process, with a peak value of 7.08 (pH), settling time of 75.16 s, rise time of 5.96 s, maximum overshoot of 10.1%, slew rate of 0.21 per second, integral square error of 0.95, integral absolute error of 0.48 and integral time absolute error of 1.88.CONCLUSIONBased on the results obtained it is observed that the NAR controller utilized with motorized control valve has better functionality in neutralizing a solution. © 2024 Society of Chemical Industry (SCI).

Analyzing nonlinear and asymmetric effects of green finance and renewable energy on energy efficiency amidst technological innovation in E7 countries

The study aims to explore the relationship between green funding, green energy, and energy efficiency in E7 countries, guided by the SDG-7 guidelines recommended by the United Nations General Assembly. Methodologically, the study employs the Nonlinear Autoregressive Distributed Lag (NARDL) and Two-Stage Least Squares (2SLS) techniques on data collected between 1988 and 2022. The rationale for this approach lies in its ability to capture both short-term and long-term dynamics in the relationship between green funding, green energy, and energy efficiency. Analysis of the data reveals varying stages of green funding growth among E7 countries, with China (1.52), Brazil (1.44), India (1.35), Indonesia (1.94), Mexico (1.73), and Russia (1.93) exhibiting different levels of progress. Russia and Turkey are identified as having the highest Gini coefficients in 2019, indicating disparities in green funding distribution within these countries. The empirical findings underscore the critical role of investment in the energy sector by both corporations and the public sector to enhance access to electricity, bolster energy security, and foster environmentally sustainable economic development. However, the study identifies insufficient investment as a fundamental obstacle hindering progress in green energy efficiency in E7 nations. Despite the potential for implementing energy efficiency measures and renewable energy sources in E7 countries, the future remains uncertain due to existing obstacles in green financing and regulatory frameworks. Consequently, the paper emphasizes the imperative of addressing these obstacles to unlock the full funding potential for energy efficiency initiatives in E7 nations.