نااطمینانی سیاست اقتصادی و تورم در ایران با رویکرد یادگیری ماشین موجک و علیت کوانتایل موجک

نویسندگان
علی مریدیان
حسن حیدری
سیدمهدی حسینی
حشمت اله عسگری
چکیده
هدف: این مطالعه به بررسی اثرات نااطمینانی سیاست اقتصادی، نرخ ارز و قیمت نفت بر تورم در ایران طی دوره زمانی 2008 تا 2023 می‌پردازد. هدف اصلی، شناسایی ماهیت کوتاه‌مدت، میان‌مدت و بلندمدت این اثرات و تحلیل پویایی‌های تورم با استفاده از روش‌های نوین موجک و یادگیری ماشین است.

روش: برای تحلیل روابط غیرخطی و وابسته به مقیاس میان متغیرها، از رگرسیون حداقل مربعات منظم‌شده با هسته موجک (WKRLS) و علیت کوانتایل ناپارامتریک موجک (WNQC) استفاده شده است. داده‌ها شامل شاخص تورم، نااطمینانی سیاست اقتصادی (EPU)، نرخ ارز غیررسمی و قیمت نفت به صورت ماهانه است. همچنین آزمون دیکی-فولر تعمیم‌یافته کوانتایل موجک (Wavelet-QADF) جهت بررسی مانایی سری‌های زمانی به کار گرفته شده است.

یافته‌ها: نتایج نشان می‌دهد متغیرهای کلیدی اقتصاد ایران در بیشتر کوانتایل‌ها و مقیاس‌های زمانی مانا هستند. بر اساس برآوردهای WKRLS، اثر نااطمینانی سیاست اقتصادی بر تورم در کوتاه‌مدت ضعیف، در میان‌مدت کاهنده اما همچنان معنادار، و در بلندمدت به‌صورت غیرخطی و شتاب‌دار افزایشی است. نرخ ارز بیشترین تأثیر را بر تورم دارد، به‌ویژه در کوتاه‌مدت به دلیل وابستگی شدید اقتصاد ایران به واردات. قیمت نفت نیز در بلندمدت اثر قابل‌توجهی بر تورم و نوسانات آن دارد. یافته‌های WNQC نشان می‌دهد که نااطمینانی سیاست اقتصادی و نرخ ارز در کوانتایل‌های پایین و میانی تورم اثر قوی‌تری دارند، در حالی که قیمت نفت عمدتاً در بلندمدت موجب تقویت نوسانات تورم می‌شود.

نتیجه‌گیری: یافته‌ها بر اهمیت ثبات سیاست‌های اقتصادی، کاهش وابستگی به درآمدهای نفتی و کنترل نوسانات نرخ ارز برای مدیریت تورم در ایران تأکید دارند. همچنین، ترکیب روش‌های موجک و یادگیری ماشین امکان ارائه تحلیلی جامع‌تر از پویایی‌های تورم را در شرایط مختلف فراهم می‌کند.
کلیدواژه‌ها
تورم
نااطمینانی سیاست اقتصادی
علیت کوانتایل موجک
یادگیری ماشین

عنوان مقاله English

Economic Policy Uncertainty and Inflation in Iran with Wavelet Machine Learning Approach and Wavelet Quantile Causality

نویسندگان English

ali moridian
Hassan Heidari
Seyed Mehdi Hosseini
Heshmatollah Asgari
چکیده English

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.

کلیدواژه‌ها English

Inflation
economic policy uncertainty
wavelet quantile causality
Machine Learning
Abid, A. (2020). Economic Policy Uncertainty and Exchange Rates in Emerging Markets: Short and Long Runs Evidence. Finance Research Letters.
Adeosun, O. A., Tabesh, M. I. Vo, X. V. & Anagreh, S. (2023). Quality & Quantity, 57, 3389-3424.
Adebayo, T. S., & Özkan, O. (2024). Evaluating the role of financial globalization and oil consumption on ecological quality: A new perspective from quantile-on-quantile granger causality. Heliyon, 10(2).
Adebayo, T. S., & Olanrewaju, V. O. (2025). Journey toward affordable and modern energy: Role of income inequality and technological innovation. Environmental Progress & Sustainable Energy, 44(2), e14555.
Ahir, H., Bloom, N., & Furceri, D. (2022). The world uncertainty index (No. w29763). National bureau of economic research.
Aisen, A. & Veiga, F.J. (2006). Political instability and inflationvolatility. IMF working paper no. 06/212.
Alola, A.A., Ozkan, ¨ O., Obekpa, H.O., (2023). Examining the patterns of disaggregate energy security risk and crude oil price: the USA scenario over 1970–2040. Resour.Pol. 82, 103514.
Al-Thaqeb, S. A., & Algharabali, B. G. (2019). Economic policy uncertainty: A literature review. The Journal of Economic Asymmetries, 20, e00133.‌
Anderl, Ch. Caporale, G. M. (2023). The Asymmetric Impact of Economic Policy and Oil Price Uncertainty on Inflation: Evidence from Developed and Emerging Economies. CESifo Working Paper, No. 10276.
Anyars, S.I. & Adabor, O. (2023). The impact of oil price changes on inflation and disaggregated inflation: insights from Ghana. Research in Globalization, 6, 1–12.
Ashiru, Y. & Oladele, L. O. (2023). Impact of Economic Policy Uncertainty on Inflation in Nigeria: Evidence from ARDL Model. Electronic copy available at: https://ssrn.com/abstract=4481113.
Athari, S., A., Kirikkaleli, D., Yousaf, I. Ali, S. (2022). Time and frequency co-movement between economic policy uncertainty and inflation: Evidence from Japan. Journal of Public Affairs, 22(Suppl. 1), e2779.
Aytaç, D. & Saraç, T. B. (2022). Economic Policy Uncertainty, Interest Rates And Inflation: Evidence From Selected Latin American Emerging Markets. Journal of Emerging Economies and Policy, 7(2) 578-590.
Balcilar, M. & Ojonugwa, U. (2018). Exchange rate and oil price pass-through to inflation in BRICS countries: Evidence from the spillover index and rolling-sample analysis. Discussion paper 15-45.
Balcilar, M., Gupta, R., Pierdzioch, C., (2016). Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test. Resour. Pol. 49, 74–80.
Beckmann, J., & Czudaj, R. L. (2025). Fundamental determinants of exchange rate expectations. International Journal of Forecasting, 41(3), 1003-1021.
Belloumi, M., Aljazea, A. & Alshehry, A. (2023). Study of the impact of crude oil prices on economic output and inflation in Saudi Arabia. Resources Policy, 86, 104179.
Bhatia, V., Das, D., Tiwari, A.K., Shahbaz, M., Hasim, H.M., (2018). Do precious metal spot prices influence each other? Evidence from a nonparametric causality-in-quantiles approach. Resour. Pol. 55, 244–252.
Bigerna, S. (2024). Connectedness analysis of oil price shocks, inflation, and exchange rate for the MENA region countries. Resources Policy, 86, 104344.
Binder, C.C. (2017). Economic policy uncertainty and household inflation uncertainty. B.E. J. Macroecon. 17 (2), 1–20.
Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623–685.
Brock, W.A., Dechert, W. D., & Scheinkman, J. A. (1987). A Test for independence based on the correlation dimension, University of Wisconsin Madison, University of Houston, and University of Chicago.
Brock, W. A., W. D. Dechert, J. A. Scheinkman, and B. LeBaron. (1996). A test for independence based on the correlation dimension. Econometric Reviews 15: 197–235. https: // doi.org / 10.1080 / 07474939608800353.
Caldara, D., Conlisk, S., Iacoviello, M. & Penn, M. (2022). Do Geopolitical Risks Raise or Lower Inflation?. Federal Reserve Board of Governors.
Carriere-Swallow, Y., Firat, M., Furceri, D., Jiménez, D., 2023. State-Dependent Exchange Rate Pass-Through. IMF Working Paper No. 2023/086
Castelnuovoو E., Pellegrino G. & Særkjær, L. L. (2023). Uncertainty and the business cycle when inflation is high. Mimeo.
Canepa, A. (2024). Inflation dynamics and persistence: The importance of the uncertainty channel. North American Journal of Economics and Finance. 72, 102135.
Chakrabarty, A., De, A., Gunasekaran, A. & Dubey, R. (2015). Investment horizon heterogeneity and wavelet: overview and further research directions. Phys. A: Stat. Mech. Appl. 45–61. https://doi.org/10. 1016/j.physa.2014.10.097.
Choi, S., Furceri, D., Loungani, P., Mishra, S. & Poplawski-Ribeiro, M. (2018). Oil price and inflation dynamics: evidence from advanced and developing economies. J. Int. Money Finance, 82, 71–96.
Çitçi, S. H. & Kaya, H. (2023). Exchange rate uncertainty and the connectedness of inflation. Borsa Istanbul Review, 23-3, 723-735.
Ding, S., Cui, T. & Zhang, Y. (2022). Futures volatility forecasting based on big data analytics with an incorporating order imbalance effect. Int. Rev. Financ. Anal, 83, 102255.
Ferwerda, J., Hainmueller, J., & Hazlett, C. J. (2017). Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls). Journal of Statistical Software, 79, 1–26. https://doi.org/10.18637/jss.v079.i03
Friedman, M. (1977). Nobel lecture: Inflation and unemployment. Journal of Political Economy, 85, 451–472.
Gao, R., Zhao, Y. & Zhang, B. (2021). The spillover effects of economic policy uncertainty on the oil, gold, and stock markets: Evidence from China. Int. J. Finance Econ, 26(2), 2134–2141
Ge, Z. & Sun, Y. (2024). Asymmetric impact of oil price shocks on inflation: Evidence from quantile-on-quantile regression. International Review of Financial Analysis, 92, 103097
Gilchrist, S., Sim, JW, Zakrajšek E. (2014). Uncertainty, financial frictions, and investment dynamics. NBER Working Papers No. 20038.
Gu, R. & Liu, S. (2022). Nonlinear analysis of economic policy uncertainty: based on the data in China, the US and the global”, Physica A: Statistical Mechanics and Its Applications, Vol. 593, 126897, doi: 10.1016/j.physa.2022.126897.
Hainmueller, J., & Hazlett, C. (2014). Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach. Political Analysis, 22(2), 143–168. https://doi.org/10.1093/pan/mpt019.
Husain, A., Yii, K.-J., & Lee, C.-C. (2023). Are green cryptocurrencies really green? New evidence from wavelet analysis. Journal of Cleaner Production, 417, 137985. https://doi.org/10.1016/j.jclepro.2023.137985
Husaini, D.H. & Lean, H.H. (2021a). Asymmetric impact of oil price and exchange rate on disaggregation price inflation. Resources Policy, 102175.
Jackson, E. A., Kamara, P. & Kamara, A. (2022). Determinants of Inflation in Sierra Leone. MPRA Paper No. 117278.
Khan, N., Saleem, A., Ozkan, O., (2023). Do geopolitical oil price risk influence stock market returns and volatility of Pakistan: evidence from novel nonparametric quantile causality approach. Resour. Pol. 81, 103355.
Knight, F. H. (1921). Risk, uncertainty and profit. Hart, Schaffner and Marx.
Kyriazis, N., Papadamou, S., Tzeremes, P. & Corbet, Sh. (2024). Examining spillovers and connectedness among commodities, inflation, and uncertainty: A quantile-VAR framework. Energy Economics, 133, 107508.
Lee, C.-C., Olasehinde-Williams, G., Ozkan, ¨ O., (2023). Geopolitical oil price uncertainty transmission into core inflation: evidence from two of the biggest global players. Energy Econ. 126, 106983.
Li, G., Li, Y., & Tsai, C. L. (2015). Quantile correlations and quantile autoregressive modeling. Journal of the American Statistical Association, 110(509), 246-261.
Ma, F., Lu, X., Wang, L. & Chevallier, J. (2021b). Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime-switching GARCH-MIDAS models. J. Forecast. 40 (6), 1070–1085.
Mo, B., Zeng, H., Meng, J. & Ding, S. (2024). The connectedness between uncertainty and exchange rates of oil import countries: new evidence from time and frequency perspective. Resources Policy, 88, 104398.
Moldovan, D., Lagoa, S. & Mendes, D. (2022). Does economic policy uncertainty imact inflation? International Academic Conference on Economics, Business and Contemporary Discussions in Social Science, 4-6 July 2022, Lisbon, Portugal.
Nasir, R. M., & He, F. (2023). Do clean energy stocks and sub-sectors hedge China economic policy uncertainty: New evidence from wavelet analysis. Journal of Cleaner Production, 429, 139385. https://doi.org/10.1016/j.jclepro.2023.139385.
Olasehinde-Williams, G., Olanipekun, I., Ozkan, ¨ O., (2023a). Stock market response to quantitative easing: evidence from the novel rolling windows nonparametric causality-in-quantiles approach. Comput. Econ.
Olasehinde-Williams, G., Ozkan, ¨ O., Akadiri, S.S., (2023b). Effects of climate policy uncertainty on sustainable investment: a dynamic analysis for the U.S. Environ. Sci. Pollut. Control Ser. 30 (19), 55326–55339.
Olanipekun, I. O., Ozkan, O., & Olasehinde-Williams, G. (2023). Is renewable energy use lowering resource-related uncertainties? Energy, 271, 126949. https://doi.org/10.1016/j.energy.2023.126949
Pham, T.A.T., Nguyen, T.T., Nasir, M.A. & Huynh, T.L.D. (2020). Exchange rate passthrough: A comparative analysis of inflation targeting & non-targeting ASEAN-5 countries. Q. Rev. Econ. Fin, 87, 158–167
Pirgaip, B., & Dinçergök, B. (2020). Economic policy uncertainty, energy consumption and carbon emissions in G7 countries: evidence from a panel Granger causality analysis. Environmental Science and Pollution Research, 27(24), 30050-30066.
Ren, Y-Sh., Klein, T., Jiang, Y., Ma, Ch-Q. & Yang, X-G. (2024). Dynamic spillovers among global oil shocks, economic policy uncertainty, and inflation expectation uncertainty under extreme shocks. Journal of International Financial Markets, Institutions & Money, 91, 101951.
Salem, L., B., Nouira, R. & Rault, Ch. (2024). On the Impact of Oil Prices on Sectoral Inflation: Evidence from World’s Top Oil Exporters and Importers. IZA DP No. 16706.
Sarker, P. K. & Wang, L. (2022). Co-movement and Granger causality between Bitcoin and M2, inflation and economic policy uncertainty: Evidence from the U.K. and Japan. Heliyon, 8, e11178.
Shao, L., Zhang, H., Chen, J., Zhu, X., (2021). Effect of oil price uncertainty on clean energy metal stocks in China: evidence from a nonparametric causality-in-quantiles approach. Int. Rev. Econ. Finance 73, 407–419.
Yolanda, Y. (2017). Analysis of Factors Affecting Inflation and its Impact on Human Development Index and Poverty in Indonesia. European Research Student, XX(4B), 38-56.
Zhao, Y., Gao, X., Zheng, H., Zhang, Y. & Sun, Q. (2025). Identifying influence pathways of oil price shocks on inflation based on impulse response networks. Energy, 314, 134107.
Živkov, D., Đurašković, J. & Manić, S. (2019). How do oil price changes affect inflation in Central and Eastern European countries? A wavelet-based Markov switching approach. Baltic Journal of Economics, 19(1), 84-104.