Forthcoming Articles

International Journal of Computational Economics and Econometrics

International Journal of Computational Economics and Econometrics (IJCEE)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Computational Economics and Econometrics (3 papers in press)

Regular Issues

  • Machine learning approach to forecasting global energy consumption and sustainability   Order a copy of this article
    by George Sammour, Anas Irsheid 
    Abstract: This study applies machine learning specifically K-means clustering and long short-term memory (LSTM) networks to analyse and forecast global energy consumption patterns. Countries were classified into 12 clusters based on energy profiles, GDP, and environmental impacts, identifying seven unique national profiles (the USA, China, France, Germany, Russia, Japan, Canada, and Brazil). These clusters revealed varying dependencies on fossil fuels and renewable sources. LSTM models predicted primary and renewable energy consumption, offering insights for energy planning and investment. Results indicated distinct reliance patterns, renewable energy forecasts were more accurate in Germany and Canada due to stable infrastructure and policies, while Russia and Brazil showed higher variability in renewable adoption. Visual analyses captured deviations during the 20082009 financial crisis and the 2020 COVID pandemic. Overall, the findings demonstrate machine learning potential to optimise energy management, support the United Nations Sustainable Development Goals, and mitigate climate change through improved forecasting and resource utilisation.
    Keywords: energy consumption forecasting; machine learning in energy; fossil fuel dependency; sustainable energy strategies; UN-SDGs; energy demand prediction; long short-term memory; LSTM; K-means clustering.
    DOI: 10.1504/IJCEE.2025.10074820
     
  • Small vs. large firms: exploring determinants of IPO launch decisions   Order a copy of this article
    by Muhammadriyaj Faniband, Pravin Jadhav 
    Abstract: This paper investigates the impact of macroeconomic and market specific factors on initial public offering (IPO) launch decisions of small and large firms in India using the count data models: Poisson and negative binomial models. We consider a monthly dataset from January 2012 to December 2024. We find that for small firms, industrial output growth is consistently positive and significant, emphasising their reliance on domestic industrial performance, while for large firms, its impact is weaker and delayed. Inflation and interest rates exert robust negative effects on both markets. Other variables, including exchange rate, economic policy uncertainty and foreign and domestic institutional investment flows, are largely insignificant. Market indicators such as Nifty 50 and market volatility influence large firms IPOs more than small firms, which reflects broader investor sensitivity. We provide several policy implications for the central bank and government to foster a conducive IPO environment for small and large Indian firms.
    Keywords: macroeconomic; market indicators; initial public offerings; IPO; SME; small firms; large firms; count models; India.
    DOI: 10.1504/IJCEE.2026.10075355
     
  • Panel models in MATLABR®: fixed effects, clustered standard errors and large datasets   Order a copy of this article
    by Pavel S. Kapinos 
    Abstract: This note describes a MATLAB® program that offers two advantages over the existing software. First, it allows for flexible estimation of multi-way clustered standard errors using the methodology of Cameron et al. (2011), which, unlike other approaches, does not limit the number of clustering dimensions. Second, it efficiently computes estimates of multi-way fixed effects models, with or without clustered standard errors, using native MATLAB® commands and materially reducing computational time in large datasets. Additional optionality, such as the ability to drop singleton observations or provide standard errors for linear combinations of estimated coefficients, is also included.
    Keywords: panel models; fixed effects; clustered standard errors; large datasets; MATLAB®.
    DOI: 10.1504/IJCEE.2026.10075536
     

 

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