The Phillips curve in Iran: econometric versus artificial neural networks

In this paper, we develop a function of inflation, unemployment, liquidity and real effective exchange rate by applying Autoregressive Distributed Lag (ARDL) and Artificial Neural Networks (ANN). We employ the aforementioned methods to derive the so-called Phillips curve. For the empirical objective...

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Bibliographic Details
Published in:Heliyon 2019-08, Vol.5 (8), p.e02344-e02344, Article e02344
Main Authors: Jalaee, Sayyed Abdolmajid, Lashkary, Mehrdad, GhasemiNejad, Amin
Format: Article
Language:English
Subjects:
ANN
ARDL
Economics
Phillips curve
Stagflation
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Summary:In this paper, we develop a function of inflation, unemployment, liquidity and real effective exchange rate by applying Autoregressive Distributed Lag (ARDL) and Artificial Neural Networks (ANN). We employ the aforementioned methods to derive the so-called Phillips curve. For the empirical objective, our primary purpose is explicitly to compare two types of the Phillips curve models obtained by ANN and the econometric methods, ARDL. Then we can check the behavior of the Phillips curve in Iran. We demonstrate that the Phillips curve for the empirical data in Iran differs slightly across ANN than econometric methods. In other words, according to the structure of Iran's economy, the ANN technique outshines the other one in terms of goodness of fit and prognosis capability. Finally, under two scenarios inflation would be forecasted in Iran up to 2025. Our findings point out that the trend of price changes in Iran would have an increasing trend in the considered period.
ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2019.e02344