Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/zbw/hwwirp/185.html
   My bibliography  Save this paper

Artificial neural network regression models: Predicting GDP growth

Author

Listed:
  • Jahn, Malte
Abstract
Artificial neural networks have become increasingly popular for statistical model fitting over the last years, mainly due to increasing computational power. In this paper, an introduction to the use of artificial neural network (ANN) regression models is given. The problem of predicting the GDP growth rate of 15 industrialized economies in the time period 1996-2016 serves as an example. It is shown that the ANN model is able to yield much more accurate predictions of GDP growth rates than a corresponding linear model. In particular, ANN models capture time trends very flexibly. This is relevant for forecasting, as demonstrated by out-of-sample predictions for 2017.

Suggested Citation

  • Jahn, Malte, 2018. "Artificial neural network regression models: Predicting GDP growth," HWWI Research Papers 185, Hamburg Institute of International Economics (HWWI).
    • Handle: RePEc:zbw:hwwirp:185
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/182108/1/1030440859.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Feng, Lihua & Zhang, Jianzhen, 2014. "Application of artificial neural networks in tendency forecasting of economic growth," Economic Modelling, Elsevier, vol. 40(C), pages 76-80.
    2. Kock, Anders Bredahl & Teräsvirta, Timo, 2014. "Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009," International Journal of Forecasting, Elsevier, vol. 30(3), pages 616-631.
    3. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
    4. Qi, Min, 2001. "Predicting US recessions with leading indicators via neural network models," International Journal of Forecasting, Elsevier, vol. 17(3), pages 383-401.
    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. Huaqing Xie & Xingcheng Xu & Fangjia Yan & Xun Qian & Yanqing Yang, 2024. "Deep Learning for Multi-Country GDP Prediction: A Study of Model Performance and Data Impact," Papers 2409.02551, arXiv.org.
    2. Simon Blöthner & Mario Larch, 2022. "Economic determinants of regional trade agreements revisited using machine learning," Empirical Economics, Springer, vol. 63(4), pages 1771-1807, October.
    3. Sabyasachi Kar & Amaani Bashir & Mayank Jain, 2021. "New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning," IEG Working Papers 446, Institute of Economic Growth.
    4. Kostadin Yotov & Emil Hadzhikolev & Stanka Hadzhikoleva & Stoyan Cheresharov, 2022. "Neuro-Cybernetic System for Forecasting Electricity Consumption in the Bulgarian National Power System," Sustainability, MDPI, vol. 14(17), pages 1-18, September.
    5. Julian Schiele & Thomas Koperna & Jens O. Brunner, 2021. "Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 65-88, February.

    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. Jahn, Malte, 2020. "Artificial neural network regression models in a panel setting: Predicting economic growth," Economic Modelling, Elsevier, vol. 91(C), pages 148-154.
    2. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis”," AQR Working Papers 201508, University of Barcelona, Regional Quantitative Analysis Group, revised Mar 2015.
    3. 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, Sciendo, vol. 6(53), pages 286-303, January.
    4. 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.
    5. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    6. Seulki Chung, 2023. "Inside the black box: Neural network-based real-time prediction of US recessions," Papers 2310.17571, arXiv.org, revised May 2024.
    7. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    8. Malte Jahn, 2023. "Artificial neural networks and time series of counts: A class of nonlinear INGARCH models," Papers 2304.01025, arXiv.org.
    9. Heravi, Saeed & Osborn, Denise R. & Birchenhall, C. R., 2004. "Linear versus neural network forecasts for European industrial production series," International Journal of Forecasting, Elsevier, vol. 20(3), pages 435-446.
    10. Qing Cao & Mark Parry & Karyl Leggio, 2011. "The three-factor model and artificial neural networks: predicting stock price movement in China," Annals of Operations Research, Springer, vol. 185(1), pages 25-44, May.
    11. Olmedo,E. & Velasco, F. & Valderas, J.M., 2007. "Caracterización no lineal y predicción no paramétrica en el IBEX35/Nonlinear Characterization and Predictions of IBEX 35," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 25, pages 815-842, Diciembre.
    12. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    13. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    14. Tea Šestanović & Josip Arnerić, 2021. "Neural network structure identification in inflation forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 62-79, January.
    15. 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.
    16. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
    17. Malte Jahn, 2023. "Regressing on distributions: The nonlinear effect of temperature on regional economic growth," Papers 2309.10481, arXiv.org.
    18. Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Bank of Finland Research Discussion Papers 14/2019, Bank of Finland.
    19. Feras A. Batarseh & Munisamy Gopinath & Anderson Monken, 2020. "Artificial Intelligence Methods for Evaluating Global Trade Flows," International Finance Discussion Papers 1296, Board of Governors of the Federal Reserve System (U.S.).
    20. Sokolov-Mladenović, Svetlana & Milovančević, Milos & Mladenović, Igor, 2017. "Evaluation of trade influence on economic growth rate by computational intelligence approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 358-362.

    More about this item

    Keywords

    neural network; forecasting; panel data;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:zbw:hwwirp:185. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/hwwiide.html .

    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.