IDEAS home Printed from https://ideas.repec.org/a/vrs/ceuecj/v6y2019i53p286-303n6.html
   My bibliography  Save this article

The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks

Author

Listed:
  • Nahapetyan Yervand

    (University of Warsaw)

Abstract

This article primarily aims to estimate the impact of the Armenian revolution and test the hypothesis, that is, the benefits of revolution and establishment of democracy can be seen even in the first year after the political change. To calculate the short-term net surplus of the revolution, we estimated the difference between the projection of Armenian economic activity for the four quarters after the revolution, using only pre-revolutionary (assuming there was no revolution) and real data for the same period after the revolution. Using deep neural network models, such as recurrent neural networks and convolutional neural networks (CNN), we compared prediction accuracy with structural econometrics, such as autoregressive integrated moving average and error correction model, using pre-revolutionary data (2000Q1–2018Q1) for Armenia and combinations of models using an ensembling mechanism. As a result, CNN overperformed the rest of the models. The CNN simulation on post-revolutionary data indicates that during the period 2018-Q2–2019-Q1, Armenia gained approximately 850 million EUR in terms of GDP, thanks to the revolution and the new government. Moreover, out of seven models, the five best models in terms of accuracy indicated that the revolution had no negative impact on the Armenian economy, as the actual values were within or above the 95% confidence interval of the prediction.

Suggested Citation

  • 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.
  • Handle: RePEc:vrs:ceuecj:v:6:y:2019:i:53:p:286-303:n:6
    DOI: 10.2478/ceej-2019-0018
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/ceej-2019-0018
    Download Restriction: no

    File URL: https://libkey.io/10.2478/ceej-2019-0018?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
    ---><---

    More about this item

    Keywords

    Armenia; revolution; GDP; neural networks; ensembling mechanism;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E02 - Macroeconomics and Monetary Economics - - General - - - Institutions and the Macroeconomy
    • P16 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Capitalist Institutions; Welfare State

    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:vrs:ceuecj:v:6:y:2019:i:53:p:286-303:n:6. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.