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Abstract: (34 Views)
Objective: This study examines the effects of economic policy uncertainty, exchange rate, and oil price on inflation in Iran during the period 2008 to 2023. The main objective is to identify the short-term, medium-term, and long-term nature of these effects and analyze inflation dynamics using modern wavelet and machine learning methods.
Materials and Methods: Regularized least squares regression with wavelet kernel (WKRLS) and nonparametric wavelet quantile causality (WNQC) are used to analyze nonlinear and scale-dependent relationships between variables. The data include inflation index, economic policy uncertainty (EPU), unofficial exchange rate, and oil price on a monthly basis. The generalized wavelet quantile Dickey-Fuller test (Wavelet-QADF) is also used to examine the stationarity of time series.
Results: The results show that key variables of the Iranian economy are stationary in most quantiles and time scales. According to WKRLS estimates, the effect of economic policy uncertainty on inflation is weak in the short run, decreasing but still significant in the medium run, and increasing non-linearly and acceleratingly in the long run. The exchange rate has the greatest impact on inflation, especially in the short run due to the Iranian economy’s heavy dependence on imports. Oil prices also have a significant impact on inflation and its volatility in the long run. WNQC findings show that economic policy uncertainty and exchange rate uncertainty have a stronger effect in the low and middle quantiles of inflation, while oil prices mainly amplify inflation fluctuations in the long run.
Conclusion: The findings emphasize the importance of stable economic policies, reducing dependence on oil revenues, and controlling exchange rate fluctuations for managing inflation in Iran. Also, combining wavelet and machine learning methods allows for a more comprehensive analysis of inflation dynamics in different conditions.
Materials and Methods: Regularized least squares regression with wavelet kernel (WKRLS) and nonparametric wavelet quantile causality (WNQC) are used to analyze nonlinear and scale-dependent relationships between variables. The data include inflation index, economic policy uncertainty (EPU), unofficial exchange rate, and oil price on a monthly basis. The generalized wavelet quantile Dickey-Fuller test (Wavelet-QADF) is also used to examine the stationarity of time series.
Results: The results show that key variables of the Iranian economy are stationary in most quantiles and time scales. According to WKRLS estimates, the effect of economic policy uncertainty on inflation is weak in the short run, decreasing but still significant in the medium run, and increasing non-linearly and acceleratingly in the long run. The exchange rate has the greatest impact on inflation, especially in the short run due to the Iranian economy’s heavy dependence on imports. Oil prices also have a significant impact on inflation and its volatility in the long run. WNQC findings show that economic policy uncertainty and exchange rate uncertainty have a stronger effect in the low and middle quantiles of inflation, while oil prices mainly amplify inflation fluctuations in the long run.
Conclusion: The findings emphasize the importance of stable economic policies, reducing dependence on oil revenues, and controlling exchange rate fluctuations for managing inflation in Iran. Also, combining wavelet and machine learning methods allows for a more comprehensive analysis of inflation dynamics in different conditions.
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