IDEAS home Printed from https://ideas.repec.org/a/rsg/littra/2016-036.html

Predicting Future GDP Development by Means of Artificial Intelligence

Author

Listed:
  • Jaromír Vrbka

Abstract

GDP measures the monetary value of the total production of goods and services in a given country over a certain period of time. GDP establishes the economic performance of a country or region and is useful for comparing differences in the living standards of nations. In general, three methods exist for determining GDP, based on production, expenditure and income. In today’s modern world, economists are looking for other appropriate and improved models for the management and prediction of all economic indicators. Artificial intelligence is among those methods that have gained in importance because of its very good predictive ability over conventional statistical and mathematical models. It consists of a time series, for example, but especially of artificial neural networks. This paper describes the known applications of artificial intelligence for the prediction of GDP and then applies neural networks to predict the GDP growth of Eurozone countries until the year 2025. The selected neural structures exhibit satisfactory numerical characteristics and are equivalent to expert evaluations of GDP development. They therefore appear to be a useful tool for predicting GDP.

Suggested Citation

  • Jaromír Vrbka, 2016. "Predicting Future GDP Development by Means of Artificial Intelligence," Littera Scripta, VSTE, vol. 9(3), pages 154-167, May.
  • Handle: RePEc:rsg:littra:2016-036
    DOI: 10.36708/LS.2016.I03.012
    as

    Download full text from publisher

    File URL: https://littera-scripta.com/articles/predicting-future-gdp-development-by-means-of-artificial-intelligence-78289a05
    Download Restriction: no

    File URL: https://littera-scripta.com/api/v1/public/files/32faf8e9-ce5c-4a06-adc0-270e71cd67c0
    Download Restriction: no

    File URL: https://libkey.io/10.36708/LS.2016.I03.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kiani, Khurshid M., 2016. "On business cycle fluctuations in USA macroeconomic time series," Economic Modelling, Elsevier, vol. 53(C), pages 179-186.
    2. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2015. "Was the recent downturn in US real GDP predictable?," Applied Economics, Taylor & Francis Journals, vol. 47(28), pages 2985-3007, June.
    3. Chai, Soo H. & Lim, Joon S., 2016. "Forecasting business cycle with chaotic time series based on neural network with weighted fuzzy membership functions," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 118-126.
    4. Tara Sinclair & H. O. Stekler & L. Kitzinger, 2010. "Directional forecasts of GDP and inflation: a joint evaluation with an application to Federal Reserve predictions," Applied Economics, Taylor & Francis Journals, vol. 42(18), pages 2289-2297.
    5. Arunraj, Nari Sivanandam & Ahrens, Diane, 2015. "A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 321-335.
    6. Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
    7. Pichler Paul, 2008. "Forecasting with DSGE Models: The Role of Nonlinearities," The B.E. Journal of Macroeconomics, De Gruyter, vol. 8(1), pages 1-35, July.
    8. Pilar Poncela & Antonio García‐Ferrer, 2014. "The Effects of Disaggregation on Forecasting Nonstationary Time Series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 300-314, July.
    9. Lehmann Robert & Wohlrabe Klaus, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, De Gruyter, vol. 16(2), pages 226-254, May.
    10. Saman, Corina, 2011. "Scenarios of the Romanian GDP Evolution With Neural Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-140, December.
    11. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
    Full references (including those not matched with items on IDEAS)

    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. Jena, Pradyot Ranjan & Majhi, Ritanjali & Kalli, Rajesh & Managi, Shunsuke & Majhi, Babita, 2021. "Impact of COVID-19 on GDP of major economies: Application of the artificial neural network forecaster," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 324-339.
    2. Jahn, Malte, 2020. "Artificial neural network regression models in a panel setting: Predicting economic growth," Economic Modelling, Elsevier, vol. 91(C), pages 148-154.
    3. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    4. Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Bayesian neural networks for macroeconomic analysis," Journal of Econometrics, Elsevier, vol. 249(PC).
    5. Constantina Kopitsa & Ioannis G. Tsoulos & Vasileios Charilogis & Athanassios Stavrakoudis, 2024. "Predicting the Duration of Forest Fires Using Machine Learning Methods," Future Internet, MDPI, vol. 16(11), pages 1-19, October.
    6. Dürmeier, Stefan, 2022. "A model of quantitative easing at the zero lower bound," BERG Working Paper Series 183, Bamberg University, Bamberg Economic Research Group.
    7. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Paradigm, vol. 6(53), pages 286-303, January.
    8. Mitze, Timo & Makkonen, Teemu, 2023. "Can large-scale RDI funding stimulate post-crisis recovery growth? Evidence for Finland during COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    9. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    10. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    11. Fritsche, Ulrich & Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2015. "Forecasting the Brazilian real and the Mexican peso: Asymmetric loss, forecast rationality, and forecaster herding," International Journal of Forecasting, Elsevier, vol. 31(1), pages 130-139.
    12. Jeon, Yunho & Seong, Sihyeon, 2022. "Robust recurrent network model for intermittent time-series forecasting," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1415-1425.
    13. Gupta, Rangan & Wohar, Mark, 2017. "Forecasting oil and stock returns with a Qual VAR using over 150years off data," Energy Economics, Elsevier, vol. 62(C), pages 181-186.
    14. Kwon, He-Boong & Lee, Jooh, 2019. "Exploring the differential impact of environmental sustainability, operational efficiency, and corporate reputation on market valuation in high-tech-oriented firms," International Journal of Production Economics, Elsevier, vol. 211(C), pages 1-14.
    15. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
    16. Barbaglia, Luca & Frattarolo, Lorenzo & Hauzenberger, Niko & Hirschbühl, Dominik & Huber, Florian & Onorante, Luca & Pfarrhofer, Michael & Pezzoli, Luca Tiozzo, 2026. "Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model," International Journal of Forecasting, Elsevier, vol. 42(2), pages 657-672.
    17. Saman, Corina, 2011. "Scenarios of the Romanian GDP Evolution With Neural Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-140, December.
    18. Hamid Baghestani, 2016. "Interest rate movements and US consumers’ inflation forecast errors: is there a link?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 40(3), pages 623-630, July.
    19. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    20. Rangan Gupta & Rudi Steinbach, 2010. "Forecasting Key Macroeconomic Variables of the South African Economy: A Small Open Economy New Keynesian DSGE-VAR Model," Working Papers 201019, University of Pretoria, Department of Economics.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:rsg:littra:2016-036. 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: Robin Kunju Mol Raj (email available below). General contact details of provider: https://littera-scripta.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.