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

Yervand Nahapetyan

Yervand Nahapetyan

University of Warsaw
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Nahapetyan, Yervand
  
08 lug 2020
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Pubblicato online: 08 lug 2020
Pagine: 286 - 303
DOI: https://doi.org/10.2478/ceej-2019-0018
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© 2019 Yervand Nahapetyan, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Quarterly GDP of Armenia.
Quarterly GDP of Armenia.

Fig. 2

Structure of manufacturing in 2017.
Structure of manufacturing in 2017.

Fig. 3

Convolutional neural network architecture.
Convolutional neural network architecture.

Fig. 4

LSTM recurrent neural network architecture.
LSTM recurrent neural network architecture.

Figure 5

ACF and PACF graphs for the SARIMA model.
ACF and PACF graphs for the SARIMA model.

Fig. 6

CNN and LSTM real versus predicted values.
CNN and LSTM real versus predicted values.

Fig. 7

CNN and ECM test predictions including post-revolutionary data.
CNN and ECM test predictions including post-revolutionary data.

Fig. 8

LSTM and SARIMA test predictions including post-revolutionary data.
LSTM and SARIMA test predictions including post-revolutionary data.

Fig. 9

Stacking, LASSO and Simple Av. test predictions including post-revolutionary data.
Stacking, LASSO and Simple Av. test predictions including post-revolutionary data.

Model error statistics

RMSEMAEMedAER2SD
CNN58266.7446161.4534190.780.977813733.60
Stacking linear58119.6546434.2948105.920.978013698.93
SARIMA66447.3947783.7836712.050.971215661.80
Stacking LASSO59349.3847907.4448731.620.977013988.78
Simple average70968.7057434.5448855.180.967116727.48
LSTM102526.2177680.8456860.310.931424165.66
ECM128851.99109558.86101529.370.891730370.71
Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Economia e business, Economia politica, Teoria economica, sistemi e strutture, Microeconomia, Macroeconomia, Politica economica
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