Template-Type: ReDIF-Article 1.0 Author-Name: Jana Kopecká Author-X-Name-First: Jana Author-X-Name-Last: Kopecká Author-Name: Lenka Viskotová Author-X-Name-First: Lenka Author-X-Name-Last: Viskotová Author-Name: David Hampel Author-X-Name-First: David Author-X-Name-Last: Hampel Title: Minimum wage as the determinant of productivity in EU countries Abstract: When introducing and setting minimum wages, primarily to reduce poverty and avoid undesirable phenomena in the labour market, it is necessary to monitor the impact on various aspects of the real economy. This paper focuses on demonstrating the positive impact of nominal minimum wage growth on productivity in EU countries. A cluster analysis is used to divide countries into two distinguished clusters. Using panel regression, the effect of a minimum wage is found to be significant and positive. To rule out spurious regressions and to demonstrate the robustness of the performed analyses, appropriate covariates are included in the models, different forms of productivity are modelled, and the models are also estimated independently for each cluster. Journal: Int. J. of Computational Economics and Econometrics Pages: 3-17 Issue: 1/2 Volume: 15 Year: 2025 Keywords: cluster analysis; company production process; EU27; human capital; labour costs; low-wage employees; minimum wage; productivity of labour; panel regression model; training of employees. File-URL: http://www.inderscience.com/link.php?id=145003 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:1/2:p:3-17 Template-Type: ReDIF-Article 1.0 Author-Name: Tomáš Heryán Author-X-Name-First: Tomáš Author-X-Name-Last: Heryán Author-Name: Petra Růčková Author-X-Name-First: Petra Author-X-Name-Last: Růčková Author-Name: Jana Šimáková Author-X-Name-First: Jana Author-X-Name-Last: Šimáková Title: Heterogeneous impacts of the COVID-19 pandemic on financial performance among European hotels Abstract: The purpose of the paper is to investigate whether there would have been differences in the change of shareholders' funds caused by the COVID-19 pandemic in Europe among medium-sized hotels. Annual data for 17 European countries have been obtained from the Bureau van Dijk Orbis database and clustered with epidemiological data from NUTS-3 regions among selected countries. Using heterogeneous difference-in-differences with cohorts, the average treatment effect on treated has been estimated with panel data. Specifically, differences between the levels of shareholders' funds and the impact of the moderation effect between return on equity and dividends during the pandemic considering the morbidity among pandemic patients in selected regions. The results have suggested that the impact of the pandemic varies between hotels with a high concentration of ownership structure having a major owner and those with a low concentration and dispersed ownership structure. Journal: Int. J. of Computational Economics and Econometrics Pages: 18-33 Issue: 1/2 Volume: 15 Year: 2025 Keywords: heterogeneous impacts; COVID-19 pandemic; European hotels; financial performance; heterogeneous DiD models; difference-in-differences; cohorts. File-URL: http://www.inderscience.com/link.php?id=145006 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:1/2:p:18-33 Template-Type: ReDIF-Article 1.0 Author-Name: Zacharoula Kalogiratou Author-X-Name-First: Zacharoula Author-X-Name-Last: Kalogiratou Author-Name: Theodoros Monovasilis Author-X-Name-First: Theodoros Author-X-Name-Last: Monovasilis Author-Name: Nicholas Tsounis Author-X-Name-First: Nicholas Author-X-Name-Last: Tsounis Author-Name: Gerassimos Bertsatos Author-X-Name-First: Gerassimos Author-X-Name-Last: Bertsatos Title: Tourism product life cycle dynamics: a computational approach to identifying tourism stages in Italy and Greece Abstract: An adaptation of the tourist area life cycle model is used to computationally identify each stage of the tourism product life cycle to explain the dynamics of tourist arrivals to Italy and Greece. It was found that the first stage of the cycle started considerably earlier in Italy than in Greece, well before WWII, while in Greece, it started during the 1950s. A new life cycle began in Greece in 2012. Italy is still in the consolidation stage and has shown growth; and this stage will continue until 2044. However, if suitable policies are applied in terms of investments in infrastructure and human capital and in marketing, this cycle can be interrupted, and a new cycle could begin directly from the development stage, where the growth rates of the number of tourist arrivals are exponential. Investing and providing services in alternative tourism may lead to this result. Journal: Int. J. of Computational Economics and Econometrics Pages: 34-48 Issue: 1/2 Volume: 15 Year: 2025 Keywords: tourism; product life cycle; Italy; Greece. File-URL: http://www.inderscience.com/link.php?id=145007 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:1/2:p:34-48 Template-Type: ReDIF-Article 1.0 Author-Name: Mirzat Ullah Author-X-Name-First: Mirzat Author-X-Name-Last: Ullah Author-Name: Kazi Sohag Author-X-Name-First: Kazi Author-X-Name-Last: Sohag Title: Correlations and volatility spillovers across cryptocurrency and stock markets: linking gold, bonds, and FRX Abstract: This study examines the connectedness among Bitcoin, gold, equity, bonds, and dollar to Ruble exchange rate volatility in the context of new developments during Russia Ukraine conflict using daily data from January 1, 2018, to May 30, 2023. Three GARCH estimation models are utilised to capture the volatility spillover effect among the underlined assets, and assess for the hedging, diversification, and safe haven properties of assets in the context of Russian financial market. The results indicate that the Bitcoin exhibits hedging ability that enables investors to diversify the risk among the underline financial assets. In addition, VaR and CVaR estimations are employed to estimate potential losses in the portfolio during the crisis, where we observe significant increase in Bitcoin investments during crisis, where negative news has a stronger impact compared to positive news, which underscores the importance of prudent asset allocation for risk mitigation. The study provides notable policy implications within the context of the ongoing crisis between Russia and Ukraine. Journal: Int. J. of Computational Economics and Econometrics Pages: 49-77 Issue: 1/2 Volume: 15 Year: 2025 Keywords: Bitcoin; gold; equity; bonds; USD/RUB exchange rate: Russian financial market; GARCH estimation; hedging and diversification. File-URL: http://www.inderscience.com/link.php?id=145010 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:1/2:p:49-77 Template-Type: ReDIF-Article 1.0 Author-Name: Amira Hakim Author-X-Name-First: Amira Author-X-Name-Last: Hakim Title: The global inflation cycle and the dollarisation system with the interlink with commodities: an application of the Bayesian network analysis Abstract: This paper investigates the dollarisation of the international monetary system within the catalyst of global inflation using the commodities under the connectedness of the selected aggregates as an intermediary within the Bayesian network model and over a time horizon during the period Q1 1984 to Q4 2020. The Bayesian network approach results reveal that energy and gold act as hedges for the financialisation of the economy and therefore for stabilising global inflation. Our findings also indicate that the capital market and cryptocurrencies do not have significant impacts on the dollarisation of the monetary system. Moreover, the findings of the study show that the significant impact of commodities stabilising the global inflation cycle seems to be significant for the dollarisation of the monetary system. Journal: Int. J. of Computational Economics and Econometrics Pages: 94-115 Issue: 1/2 Volume: 15 Year: 2025 Keywords: global inflation; dollarisation; oil; gold; Bayesian network. File-URL: http://www.inderscience.com/link.php?id=145012 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:1/2:p:94-115 Template-Type: ReDIF-Article 1.0 Author-Name: T.D. Dang Author-X-Name-First: T.D. Author-X-Name-Last: Dang Author-Name: M.T. Nguyen Author-X-Name-First: M.T. Author-X-Name-Last: Nguyen Title: Unpacking customer feedback and brand equity dynamics in the hospitality industry through machine learning techniques Abstract: This study utilises Latent Dirichlet allocation (LDA) and latent semantic analysis (LSA) for advanced topic modelling in the hospitality sector, analysing customer feedback from Booking.com in Ho Chi Minh City, Vietnam. It highlights crucial aspects influencing brand equity: ambient noise levels, room standards, facility provisions, staff interactions, and strategic location advantages. Further, the research integrates an extensive suite of machine learning (ML) and deep learning (DL) techniques, including logistic regression (LR), random forest (RF), multinomial Naive Bayes (NB), long short-term memory (LSTM), convolutional neural network (CNN), and notably, the dense model. The dense model stands out, demonstrating remarkable performance with an accuracy rate of 0.95 and an F1-score of 0.97, validating the effectiveness of data-driven methodologies in extracting nuanced customer sentiments. These insights offer a multifaceted understanding, serving as a valuable resource for practitioners to refine service strategies, elevate customer satisfaction, and strengthen market presence. Journal: Int. J. of Computational Economics and Econometrics Pages: 78-93 Issue: 1/2 Volume: 15 Year: 2025 Keywords: customer feedback; brand equity; sentiment analysis; topic modelling; hospitality industry; machine learning. File-URL: http://www.inderscience.com/link.php?id=145014 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:1/2:p:78-93 Template-Type: ReDIF-Article 1.0 Author-Name: Dechassa Obsi Gudeta Author-X-Name-First: Dechassa Obsi Author-X-Name-Last: Gudeta Title: Application of Bayesian methods in the analysis of dynamic conditional correlation multivariate GARCH models Abstract: The study investigates the use and performance of the multivariate generalised autoregressive conditional heteroscedastic (MGARCH) model, specifically the dynamic conditional correlation (DCC)-MGARCH model in Bayesian framework. It uses a Markov chain Monte Carlo strategy and the Metropolis-Hastings algorithm for effective posterior sampling. The model is found to be more flexible and can describe uncertainties and volatilities of the error distribution. The sensitivity test shows that posterior results are more reliable when prior parameters are randomly sampled from the beta distribution. Journal: Int. J. of Computational Economics and Econometrics Pages: 116-146 Issue: 1/2 Volume: 15 Year: 2025 Keywords: Bayesian inference; dynamic conditional correlation; DCC; generalised error distribution; GED; Markov chain Monte Carlo; MCMC; Metropolis-Hastings; generalised autoregressive conditional heteroscedastic; skewed distributions. File-URL: http://www.inderscience.com/link.php?id=145018 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:1/2:p:116-146 Template-Type: ReDIF-Article 1.0 Author-Name: Fatemeh Asadi Author-X-Name-First: Fatemeh Author-X-Name-Last: Asadi Author-Name: Hamzeh Torabi Author-X-Name-First: Hamzeh Author-X-Name-Last: Torabi Author-Name: Hossein Nadeb Author-X-Name-First: Hossein Author-X-Name-Last: Nadeb Title: A new approach for independent component analysis and its application for clustering the economic data Abstract: In conventional independent component analysis (ICA) algorithms, the definition of the objective function is typically based on specific dependency criteria. The choice of these criteria significantly influences the performance of the algorithm. This article introduces a general class of dependency criteria, which is based on the cumulative distribution function, to characterise the independence of two variables. Furthermore, an applicable ICA algorithm, grounded in this class and utilising a non-parametric estimator, is proposed. The performance of the proposed algorithm is evaluated and compared with several well-known traditional algorithms, using Amari error estimation calculation as a benchmark. The proposed algorithms have been applied to a real-time series data, serving as a pre-processing clustering method. Journal: Int. J. of Computational Economics and Econometrics Pages: 147-171 Issue: 1/2 Volume: 15 Year: 2025 Keywords: Amari error; clustering; dependence criteria; independent components analysis. File-URL: http://www.inderscience.com/link.php?id=145019 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:1/2:p:147-171 Template-Type: ReDIF-Article 1.0 Author-Name: Kalva Sudhakar Author-X-Name-First: Kalva Author-X-Name-Last: Sudhakar Author-Name: Satuluri Naganjaneyulu Author-X-Name-First: Satuluri Author-X-Name-Last: Naganjaneyulu Title: An optimised CNN-stacked LSTM neural network model for predicting stock market time-series data Abstract: Stock market analysis and prediction are crucial for understanding business ownership and financial performance, this study proposes an optimised CNN-stacked LSTM neural network model for accurate stock market trend prediction. The initial challenge lies in designing a customised CNN-stacked LSTM model for stock data prediction due to the abundance of non-optimised algorithms. To address this, we conducted training and testing using diverse datasets, including NYSE, NASDAQ, and NIFTY-50, observing variations in model accuracy based on the dataset. Remarkably, our model demonstrated exceptional performance with the NIFTY-50 dataset, accurately predicting up to 99% of stocks even during the testing phase. Throughout training and validation, we measured mean squared error (MSE) values ranging from 0.001 to 0.05 and 0.002 to 0.1, depending on the dataset. Our proposed CNN-stacked LSTM model presents a promising solution for accurate prediction of stock market trends, addressing the limitations of previous methods. Journal: Int. J. of Computational Economics and Econometrics Pages: 196-224 Issue: 1/2 Volume: 15 Year: 2025 Keywords: stock market prediction; CNN-stacked LSTM model; time-series data; NYSE; NASDAQ; NIFTY; mean squared error; MSE; mean absolute error; MAE. File-URL: http://www.inderscience.com/link.php?id=145022 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:1/2:p:196-224 Template-Type: ReDIF-Article 1.0 Author-Name: Ahmar Qasim Qazi Author-X-Name-First: Ahmar Qasim Author-X-Name-Last: Qazi Author-Name: Umair Saeed Bhutta Author-X-Name-First: Umair Saeed Author-X-Name-Last: Bhutta Author-Name: Muhammad Rehan Shaukat Author-X-Name-First: Muhammad Rehan Author-X-Name-Last: Shaukat Author-Name: Amitabh Mishra Author-X-Name-First: Amitabh Author-X-Name-Last: Mishra Title: Disaggregated productivity measurement of industrial firms using the data envelopment analysis method Abstract: Firms in the industrial sector in the Sultanate of Oman need to strategise by measuring and evaluating their current status regarding productivity, efficiency, and technology. Productivity measurement was conducted using data envelope analysis with data from 31 decision-making units between 2015 and 2020. It was observed that productivity performance deteriorated over this period. The average efficiency change was measured at 2.14%. Furthermore, industrial firms, on average, performed well in determining the efficiency frontier, with a measured change of 0.54%. Additionally, the calculated decomposed efficiency change score suggests that while the industrial sector is generally effective in scaling operations, it faces challenges in utilising inputs effectively. This indicates that the industrial sector did not manage its resources efficiently during the sample period. Journal: Int. J. of Computational Economics and Econometrics Pages: 172-195 Issue: 1/2 Volume: 15 Year: 2025 Keywords: productivity growth; data envelopment analysis; DEA; technology; efficiency. File-URL: http://www.inderscience.com/link.php?id=145024 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:1/2:p:172-195 Template-Type: ReDIF-Article 1.0 Author-Name: Bruno Tag Sales Author-X-Name-First: Bruno Tag Author-X-Name-Last: Sales Author-Name: Hudson S. Torrent Author-X-Name-First: Hudson S. Author-X-Name-Last: Torrent Author-Name: Rangan Gupta Author-X-Name-First: Rangan Author-X-Name-Last: Gupta Title: Forecasting real housing price returns of the USA using machine learning: the role of climate risks Abstract: Climate change, a pressing global challenge, has wide-ranging implications for various aspects of our lives, including housing prices. This paper delves into the complex relationship between climate change and real housing price returns in the USA, leveraging a comprehensive dataset and advanced machine learning technique - the stepwise boosting method. This ensemble learning technique significantly enhances our analysis. Our findings suggest that climate change variables can influence real housing price returns, particularly in the short term, but the relationship is complex and varies by region. The adaptive learning capability of step-wise boosting has been crucial in uncovering these insights. This methodological approach not only underscores the importance of employing advanced predictive models in analysing the effects of climate change on urban development but also highlights the potential for informed decision-making, sustainable urban planning, and climate risk mitigation. Journal: Int. J. of Computational Economics and Econometrics Pages: 225-246 Issue: 3 Volume: 15 Year: 2025 Keywords: climate finance; housing market; machine learning; predictive modelling; step-wise boosting; USA. File-URL: http://www.inderscience.com/link.php?id=147775 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:3:p:225-246 Template-Type: ReDIF-Article 1.0 Author-Name: Dipankar Das Author-X-Name-First: Dipankar Author-X-Name-Last: Das Author-Name: Shameek Mukhopadhyay Author-X-Name-First: Shameek Author-X-Name-Last: Mukhopadhyay Title: Comparative analysis of automatic time-series forecasting approaches for potato wholesale price index in India Abstract: This paper investigates the effectiveness of 11 automatic time-series forecasting techniques in forecasting the wholesale price index (WPI) of potatoes in India. Techniques include autoregressive integrated moving average (ARIMA), error-trend-seasonality (ETS), four artificial neural network (ANN) models, and five hybrid approaches. Evaluation is based on mean absolute percentage error (MAPE). The forecast horizon extends up to 15 months. This work revealed that the ETS-ANN method is the most effective, showcasing an average MAPE of 5.42%. The improvement of the forecast accuracy of the hybrid ETS-ANN over the naive (baseline) is 59.8%, ETS is 29.18%, and ANN is 41.85%. It indicates a significant enhancement in forecast accuracy. The ETS-ANN approach exhibited statistically significant results. It validates the ETS-ANN technique's effectiveness in accurately forecasting the potato WPI in India. It contributes to this specific domain and provides valuable insights for policymakers and stakeholders. Additionally, it may serve as a methodological guide for other agricultural commodities. Journal: Int. J. of Computational Economics and Econometrics Pages: 247-264 Issue: 3 Volume: 15 Year: 2025 Keywords: time-series forecasting; automatic forecasting; agricultural economics; potato wholesale price index; hybrid ETS-ANN; India. File-URL: http://www.inderscience.com/link.php?id=147776 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:3:p:247-264 Template-Type: ReDIF-Article 1.0 Author-Name: Kala Nisha Gopinathan Author-X-Name-First: Kala Nisha Author-X-Name-Last: Gopinathan Author-Name: Punniyamoorthy Murugesan Author-X-Name-First: Punniyamoorthy Author-X-Name-Last: Murugesan Author-Name: Hari Hara Krishna Kumar Viswanathan Author-X-Name-First: Hari Hara Krishna Kumar Author-X-Name-Last: Viswanathan Author-Name: Matthew Mitchell Author-X-Name-First: Matthew Author-X-Name-Last: Mitchell Title: Novel variants of the TOPSIS algorithm to select and rate the bank counterparties Abstract: Credit rating agencies (CRAs) assign ratings to banks using the through-the-cycle (TTC) approach, which often fails to reflect the current condition of banks. Selecting bank counterparties is crucial in the derivatives market, with credit ratings typically guiding this choice. This study introduces two innovative variants of the technique for order of preference by similarity to the ideal solution (TOPSIS) for selecting and rating bank counterparties. These variants, TOPSIS1 and TOPSIS2, depart from the traditional TTC approach by using point-in-time analysis. We analyse the TOPSIS scores and rankings using statistical measures like Spearman's rank correlation coefficient. The results show that TOPSIS2 is a practical, interpretable method for rating unrated banks, predicting upgrades/downgrades, and mitigating counterparty credit risk (CCR). Journal: Int. J. of Computational Economics and Econometrics Pages: 265-293 Issue: 3 Volume: 15 Year: 2025 Keywords: OTC derivatives; credit ratings; counterparty risk mitigation strategy; TOPSIS; multi-criteria decision-making; MCDM; credit support annex; CSA. File-URL: http://www.inderscience.com/link.php?id=147777 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:3:p:265-293 Template-Type: ReDIF-Article 1.0 Author-Name: Manuel de Mier Author-X-Name-First: Manuel de Author-X-Name-Last: Mier Author-Name: Fernando Delbianco Author-X-Name-First: Fernando Author-X-Name-Last: Delbianco Author-Name: Fernando Tohmé Author-X-Name-First: Fernando Author-X-Name-Last: Tohmé Title: Causality clubs: grouping countries with cluster causality detection Abstract: This paper explores the heterogeneity of causal structures of economic growth among countries by proposing a two-step procedure. First, we apply a causal discovery technique to uncover the underlying causal structure for each country. Second, we employ hierarchical clustering over these estimates to identify groups of similar countries in terms of their causal relationships. We obtain five 'causality clubs', each one associated with a different structure of causal determinations of the growth process. We find that the usual associations between income or geographical location and the nature of economic growth processes may not always hold true. Journal: Int. J. of Computational Economics and Econometrics Pages: 294-312 Issue: 3 Volume: 15 Year: 2025 Keywords: causality clubs; cluster causality detection; causal structure; heterogeneity; economic growth. File-URL: http://www.inderscience.com/link.php?id=147779 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:3:p:294-312 Template-Type: ReDIF-Article 1.0 Author-Name: M. Burak Erturan Author-X-Name-First: M. Burak Author-X-Name-Last: Erturan Title: Causal impact of COVID-19 pandemic on international trade: Bayesian structural time series analysis with different income groups of countries Abstract: The outbreak of COVID-19 is thought to affected whole world in different scopes and by various measures. From economics to social life, politics to technology, almost all areas are estimated to be affected by COVID-19 pandemic directly or indirectly. This study aims to quantify the effects of COVID-19 pandemic on international trade in a global perspective. Exports and imports of 15 selected countries are examined with monthly values and Bayesian structural time series model is applied for causal impact analysis. Using International Money Fund's fiscal monitor, countries are selected such that there are five countries from each income level (advanced economies, middle-income emerging economies and low-income developing economies). Results suggest that although each country shows slightly different characteristics, international trade is negatively affected by the pandemic in general. Moreover, in terms of average cumulative effects, advanced economies are impacted negatively the most, while middle-income emerging economies are the second. Journal: Int. J. of Computational Economics and Econometrics Pages: 313-331 Issue: 3 Volume: 15 Year: 2025 Keywords: causal impact analysis; COVID-19 pandemic; Bayesian structural time series; BSTS; international trade. File-URL: http://www.inderscience.com/link.php?id=147780 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:3:p:313-331 Template-Type: ReDIF-Article 1.0 Author-Name: Igor Kirshin Author-X-Name-First: Igor Author-X-Name-Last: Kirshin Title: Assessment of healthy life years factors across European countries based on neural networks analysis Abstract: The objective of this paper is to identify, test and evaluate the influence of health and disability factors on the healthy life years. Panel data from the Eurostat European Health Survey and Health Statistics covering 31 European countries from 2011 to 2022 were used to examine how healthy life years are associated with health and disability factors. A cross-country multiple regression analysis with dummy variables for the COVID-19 period was performed using the multiple linear regression model and the multilayer perceptron neural network in two versions: regression and time series (regression). The results obtained convincingly confirm the proposed hypothesis: healthy life years were significantly associated with self-assessed disability level and self-assessed long-term limitations in usual activities due to health problems, and to a lesser extent with share of people with good or very good perceived health and people with long-term diseases or health problems. Global sensitivity analysis showed that all networks determine the level of disability variable to be the most important. To test the robustness of the model, the random forest model was applied. The identified factors can be used as significant predictors of healthy life years assessment for European countries population. Journal: Int. J. of Computational Economics and Econometrics Pages: 333-367 Issue: 4 Volume: 15 Year: 2025 Keywords: healthy life years; time series analysis and forecasting; multiple linear regression analysis; neural networks; global sensitivity analysis; health inequalities; self-reported health. File-URL: http://www.inderscience.com/link.php?id=150005 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:4:p:333-367 Template-Type: ReDIF-Article 1.0 Author-Name: Mouna Rekik Author-X-Name-First: Mouna Author-X-Name-Last: Rekik Author-Name: Foued Saâdaoui Author-X-Name-First: Foued Author-X-Name-Last: Saâdaoui Title: Econometric evidence on the moderating role of board composition in the CSR - financial performance Nexus Abstract: This study explores the relationship between corporate social responsibility (CSR) and firm financial performance, focusing on how board composition influences this link. It examines how governance features of the board of directors support stakeholder interests and align with strategic company goals. The analysis uses the feasible generalised least squares (FGLS) estimator, an advanced econometric method that corrects for heteroscedasticity and serial correlation to produce reliable estimates. The study analyses a panel of 349 European firms from 2011 to 2021, measuring CSR through environmental, social, and governance (ESG) scores and their key dimensions. Results show a significant positive association between CSR and financial performance, measured by return on assets and equity. Board characteristics such as size, independence, and gender diversity strengthen this relationship, while chief executive officer (CEO) duality weakens it. These findings highlight the critical role of sound corporate governance in maximising the value created by CSR initiatives. Journal: Int. J. of Computational Economics and Econometrics Pages: 368-391 Issue: 4 Volume: 15 Year: 2025 Keywords: corporate social responsibility; CSR; firm's financial performance; corporate governance; moderating effects; FGLS regression. File-URL: http://www.inderscience.com/link.php?id=150009 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:4:p:368-391 Template-Type: ReDIF-Article 1.0 Author-Name: Radhika Prosad Datta Author-X-Name-First: Radhika Prosad Author-X-Name-Last: Datta Title: Investigating efficiency dynamics of the Indian forex market using Hurst exponent and changepoint analysis: impact of financial upheavals Abstract: The foreign exchange (forex) market's efficiency has significant implications for investment decisions, risk management, and economic policies. This paper investigates the efficiency of the Indian forex market during financial upheavals, focusing on the Hurst exponent a measure from fractal theory analysis, as a tool to analyse the self-similarity and predictability of price trends. Using data from four major exchange rates (USD, GBP, EUR and JPY) against the Indian rupee, spanning 2018 to 2021, the study employs the application of the Hurst exponent, and changepoint analysis to assess the forex market's efficiency during financial upheavals, such as the COVID-19 pandemic. The study contributes to the understanding of market efficiency dynamics, offering practical implications for traders, investors, policymakers, and businesses dealing with foreign exchange rates. By bridging theoretical insights with empirical findings, this research aids decision-makers in navigating the complexities of the ever-changing landscape of international finance. Journal: Int. J. of Computational Economics and Econometrics Pages: 421-436 Issue: 4 Volume: 15 Year: 2025 Keywords: exchange rates; Hurst exponent; changepoint; market efficiency; time series. File-URL: http://www.inderscience.com/link.php?id=150012 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:4:p:421-436 Template-Type: ReDIF-Article 1.0 Author-Name: Fernando Delbianco Author-X-Name-First: Fernando Author-X-Name-Last: Delbianco Author-Name: Andrés Fioriti Author-X-Name-First: Andrés Author-X-Name-Last: Fioriti Author-Name: Fernando Tohmé Author-X-Name-First: Fernando Author-X-Name-Last: Tohmé Title: An assessment tool for academic research evaluation Abstract: The academic evaluation of the publication record of researchers is relevant for identifying both relevant topics and talented candidates for promotion and funding. A key tool for this is the use of the indexes provided by <i>Web of Science</i> and <i>Scopus</i>, costly databases that sometimes exceed the possibilities of academic institutions in many parts of the world. We develop a methodology that uses data in one of the databases to infer the most commonly used index of the <i>other</i> one. In this way, access to just <i>one</i> database allows recovering the information contained in <i>both</i>. Using machine learning methods, we select just a few of the hundreds of variables in one database, which are used in a panel regression to infer the main index in the other database. Since the information of <i>Scopus</i> can be freely scraped from the web, this approach allows the inference of the <i>impact factor</i> of publications (the main index in Web of Science), a key index used to assess the quality of academic research around the globe. Journal: Int. J. of Computational Economics and Econometrics Pages: 392-420 Issue: 4 Volume: 15 Year: 2025 Keywords: scholar indexes; bibliometrics; academic evaluation; data analysis. File-URL: http://www.inderscience.com/link.php?id=150015 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:4:p:392-420 Template-Type: ReDIF-Article 1.0 Author-Name: Chiraz Lakhal Author-X-Name-First: Chiraz Author-X-Name-Last: Lakhal Author-Name: Imen Zorgati Author-X-Name-First: Imen Author-X-Name-Last: Zorgati Title: Russia-Ukraine conflict, commodities and stock market: DCC-GARCH approach Abstract: This study examines the influence of the Russia-Ukraine conflict on the relationship between wheat prices and the stock markets of the G7 countries, employing the DCC-GARCH model with daily data spanning from January 1, 2020, to September 29, 2023. The results reveal a significant negative shock in the dynamic conditional correlation between wheat and the stock markets analysed, particularly in France, Germany, and Italy. These findings suggest that wheat can serve as an effective tool for mitigating portfolio risk. This research sheds light on the effects of geopolitical events on the dynamics of financial markets, especially in advanced economies. The findings have important implications for both portfolio management and policy formulation. Investors in stocks and commodities may benefit from the policy insights provided by this study, which could assist them in making informed investment decisions during periods of heightened volatility. Journal: Int. J. of Computational Economics and Econometrics Pages: 437-457 Issue: 4 Volume: 15 Year: 2025 Keywords: Russia-Ukraine war; stock market; wheat; DCC-GARCH model. File-URL: http://www.inderscience.com/link.php?id=150023 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:15:y:2025:i:4:p:437-457