IDEAS home Printed from https://ideas.repec.org/a/spt/stecon/v6y2017i4f6_4_2.html
   My bibliography  Save this article

Forecasting a Composite Indicator of Economic Activity in Ghana: A Comparison of Data Science Methods

Author

Listed:
  • Emmanuel Thompson
  • Ahmad M. Talafha

Abstract

Of recent, data science methods have been used to study and forecast financial and economic problems. This paper uses historical data to build a more parsimonious predictive model for making short term forecasts of the future values for the Composite Indicator of Economic Activity (CIEA) in Ghana. Based on our studies of a variety of shrinkage methods and a dimension reduction technique, we show empirically that the estimated model based on the Adaptive Elastic Net (Adaptive ENET) algorithm offers the greatest forecasting potential for the CIEA. A major finding in this paper was that, the Adaptive ENET model outperformed the benchmark model: Principal Component Regression (PCR) according to the cross validation root mean square error difference Statistic.Mathematics Subject Classification: G12; C15; G22Keywords: Composite Index of Economic Activity; Least Absolute Shrinkage and Selection Operator; Elastic Net; Principal Component; Artificial Neural Network

Suggested Citation

  • Emmanuel Thompson & Ahmad M. Talafha, 2017. "Forecasting a Composite Indicator of Economic Activity in Ghana: A Comparison of Data Science Methods," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 6(4), pages 1-2.
    • Handle: RePEc:spt:stecon:v:6:y:2017:i:4:f:6_4_2
    as

    Download full text from publisher

    File URL: http://www.scienpress.com/Upload/JSEM%2fVol%206_4_2.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    2. Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
    3. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Boateng, Ebenezer & Asafo-Adjei, Emmanuel & Addison, Alex & Quaicoe, Serebour & Yusuf, Mawusi Ayisat & Abeka, Mac Junior & Adam, Anokye M., 2022. "Interconnectedness among commodities, the real sector of Ghana and external shocks," Resources Policy, Elsevier, vol. 75(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luke Hartigan & James Morley, 2020. "A Factor Model Analysis of the Australian Economy and the Effects of Inflation Targeting," The Economic Record, The Economic Society of Australia, vol. 96(314), pages 271-293, September.
    2. Georges Bresson & Jean-Michel Etienne & Pierre Mohnen, 2011. "How important is innovation? A Bayesian factor-augmented productivity model on panel data," TEPP Working Paper 2011-06, TEPP.
    3. Jiahe Lin & George Michailidis, 2019. "Approximate Factor Models with Strongly Correlated Idiosyncratic Errors," Papers 1912.04123, arXiv.org.
    4. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
    5. Yunus Emre Ergemen & Carlos Vladimir Rodríguez-Caballero, 2016. "A Dynamic Multi-Level Factor Model with Long-Range Dependence," CREATES Research Papers 2016-23, Department of Economics and Business Economics, Aarhus University.
    6. Skripnikov, A. & Michailidis, G., 2019. "Joint estimation of multiple network Granger causal models," Econometrics and Statistics, Elsevier, vol. 10(C), pages 120-133.
    7. Claudio Barbieri & Mattia Guerini & Mauro Napoletano, 2021. "The anatomy of government bond yields synchronization in the Eurozone," SciencePo Working papers Main hal-03373853, HAL.
    8. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
    9. Rua, António, 2017. "A wavelet-based multivariate multiscale approach for forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 581-590.
    10. Jacobs, Jan P.A.M. & Otter, Pieter W. & den Reijer, Ard H.J., 2012. "Information, data dimension and factor structure," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 80-91.
    11. Samuel N. Cohen & Silvia Lui & Will Malpass & Giulia Mantoan & Lars Nesheim & 'Aureo de Paula & Andrew Reeves & Craig Scott & Emma Small & Lingyi Yang, 2023. "Nowcasting with signature methods," Papers 2305.10256, arXiv.org.
    12. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    13. repec:dau:papers:123456789/11663 is not listed on IDEAS
    14. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    15. Uribe, Jorge M. & Chuliá, Helena & Guillén, Montserrat, 2017. "Uncertainty, systemic shocks and the global banking sector: Has the crisis modified their relationship?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 52-68.
    16. Hallin, Marc & Lippi, Marco, 2013. "Factor models in high-dimensional time series—A time-domain approach," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2678-2695.
    17. Bennett, Donyetta & Mekelburg, Erik & Strauss, Jack & Williams, T.H., 2024. "Unlocking the black box of sentiment and cryptocurrency: What, which, why, when and how?," Global Finance Journal, Elsevier, vol. 60(C).
    18. Bräuning, Falk & Koopman, Siem Jan, 2014. "Forecasting macroeconomic variables using collapsed dynamic factor analysis," International Journal of Forecasting, Elsevier, vol. 30(3), pages 572-584.
    19. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    20. Hammerschmid, Regina & Lohre, Harald, 2018. "Regime shifts and stock return predictability," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 138-160.
    21. Laurent Callot & Johannes Tang Kristensen, 2016. "Regularized Estimation of Structural Instability in Factor Models: The US Macroeconomy and the Great Moderation," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 437-479, Emerald Group Publishing Limited.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spt:stecon:v:6:y:2017:i:4:f:6_4_2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Eleftherios Spyromitros-Xioufis (email available below). General contact details of provider: http://www.scienpress.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.