Jalaee Sayyed Abdolmajid, Lashkary Mehrdad, GhasemiNejad Amin
Shahid Bahonar University of Kerman, Iran.
Heliyon. 2019 Aug 23;5(8):e02344. doi: 10.1016/j.heliyon.2019.e02344. eCollection 2019 Aug.
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.
在本文中,我们通过应用自回归分布滞后(ARDL)和人工神经网络(ANN)来构建通货膨胀、失业、流动性和实际有效汇率的函数。我们采用上述方法来推导所谓的菲利普斯曲线。出于实证目的,我们的主要目的是明确比较通过人工神经网络和计量经济学方法(ARDL)获得的两种菲利普斯曲线模型。然后我们可以检验伊朗菲利普斯曲线的表现。我们证明,伊朗实证数据的菲利普斯曲线在人工神经网络方法下与计量经济学方法略有不同。换句话说,根据伊朗的经济结构,人工神经网络技术在拟合优度和预测能力方面优于另一种方法。最后,在两种情景下对伊朗2025年前的通货膨胀进行预测。我们的研究结果指出,在考虑的时期内,伊朗价格变化趋势将呈上升趋势。