International Journal of Computational Economics and Econometrics (6 papers in press)

Regular Issues

• Forecasting real housing price returns of the USA using machine learning: the role of climate risks  
  by Bruno Tag Sales, Hudson S. Torrent, Rangan Gupta 
  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.
  Keywords: climate finance; housing market; machine learning; predictive modelling; step-wise boosting; USA.
  
  
• Comparative analysis of automatic time-series forecasting approaches for potato wholesale price index in India  
  by Dipankar Das, Shameek Mukhopadhyay 
  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 techniques 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.
  Keywords: time-series forecasting; automatic forecasting; agricultural economics; potato wholesale price index; hybrid ETS-ANN; India.
  
  
• Novel variants of the TOPSIS algorithm to select and rate the bank counterparties  
  by Kala Nisha Gopinathan, Punniyamoorthy Murugesan, Hari Hara Krishna Kumar Viswanathan, Matthew Mitchell 
  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 Spearmans 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).
  Keywords: OTC derivatives; credit ratings; counterparty risk mitigation strategy; TOPSIS; multi-criteria decision-making; MCDM; credit support annex; CSA.
  
  

Special Issue on: Economic Analysis and the Current Real-World Situation Exploring New Trends in Applied Economics. Special Issue in Honour of Prof. George Agiomirgianakis

• Investment analytics using association rule mining (Finassociations)  
  by Elif Kartal, M. Erdal Balaban, Zeki Özen 
  Abstract: This study aims to discover financial associations (relations) in (foreign) exchange rates, cryptocurrencies, and stocks using Association Rule Mining (ARM). It demonstrates the applicability and success of ARM on alternative investment instruments over desired periods. A dynamic web application called “Finassociations” was developed in this scope, allowing investors to use and discover ARM. They can use the desired filters to make investment decisions by generating rules for which investment instruments rise or fall together. The application dynamically retrieves current data from Yahoo Finance. This study is a dynamic and expanded update on the existing ones. The exemplary analyses utilized data spanning various periods, up to two years preceding October 9, 2022. According to the study results, significant and strong financial associations in three different investment groups can be obtained. Also, the results show that short-term financial data can be preferred over long-term financial data when examining associations between investment instruments.
  Keywords: association rule mining; ARM; association rules; data mining; apriori; investment decisions; financial associations; finance; foreign exchange rates; cryptocurrencies; stocks.
  
  
• Current account dynamics in selected Southeast Asian economies, using a PSVAR model  
  by Minoas Koukouritakis 
  Abstract: The present paper explores the impact of budget balance shocks, as well as output shocks, on the current account balance of four high-income Southeast Asian countries, namely China, Japan, Republic of Korea and Singapore. For performing this analysis, a panel structural VAR model has been implemented, using an extended sample of a more than 40-year period. The estimated impulse-response functions and variance decompositions for common and idiosyncratic shocks provide an indication regarding the way that fiscal and output shocks affect the current account balance. In brief, they imply that, in the short run, the twin divergence hypothesis holds. In other words, an expansionary fiscal policy will improve the current account balance. However, in the long run, the empirical evidence seems to validate the new classical Ricardian equivalence theorem.
  Keywords: current account balance; budget balance; panel SVAR model; impulse responses; structural variance decompositions.
  
  
• Social cohesion as a determinant of economic activity amidst the crises of the 21st century in EU countries  
  by Demosthenes Georgopoulos, Theodore Papadogonas, George Sfakianakis 
  Abstract: In this paper, we investigate the potential impact of social cohesion on the level of economic activity in European Union countries for the 2001-2022 period. Following a macroeconomic approach we consider the effect of inequality on economic activity using income per capita, competitiveness, public debt and deficit and monetary policy as control variables. For the whole period under investigation, we observe that all but one (competitiveness) explanatory variables are statistically significant, also bearing the expected sign. Particularly interesting, though, is the strong and positive relationship between inequality and unemployment. Even more interesting though is that we observe a change in the effect of inequality on unemployment before and after the 2007-2009 crisis when during that second period inequality became the most significant determinant of unemployment, while in the pre-crisis period it was insignificant. Our approach supports the rekindled interest placed on inequality as an important factor affecting social welfare after the great recession.
  Keywords: unemployment; economic crisis; inequality; competitiveness; public debt; public deficit; GDP per capita; monetary policy.