International Journal of New Political Economy

International Journal of New Political Economy

The Impact of Cyberspace Uncertainty on Inflation in Iran: Evidence from a Johansen–VECM Model

Articles in Press

Document Type : Original Article

Authors
Diba Ezatpour 1
Zana Mozaffari 2
Ramin Amani 3
1 M.Sc in Economics., Department of Economics, Faculty of Humanities and Social Sciences, University of Kurdistan, Sanandaj, Iran.
2 Assistant Professor, Department of Economics, Faculty of Humanities and Social Sciences, University of Kurdistan, Sanandaj, Iran.
3 Ph.D. Candidate in Economics, Department of Economic Development and Planning, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
10.48308/jep.2026.243748.1266
Abstract
This study examines the dynamic relationship between cyberspace uncertainty and inflation in Iran using 56 quarterly observations from 2011Q1 to 2024Q4. Cyberspace uncertainty is represented by a reproducible composite SMU proxy derived from the updated Twitter Economic Uncertainty (TEU) series: the four published variants (TEU-USA, TEU-SCA, TEU-WGT, and TEU-ENG) are aligned by date, averaged arithmetically, aggregated by within-quarter means, and log-transformed for estimation. Multivariate cointegration is assessed with the Johansen–Juselius system approach, followed by a rank-two Vector Error Correction Model (VECM) and generalized impulse-response functions. A theory-guided general-to-specific restriction of the inflation equation is supported by a joint Wald test; the retained adjustment term, lagged inflation change, lagged change in cyberspace uncertainty, and lagged liquidity growth are statistically significant at the 5% level or better. The central dynamic result is horizon-dependent: an SMU innovation lowers inflation on impact and over the first few quarters, but the response crosses zero after roughly six quarters and becomes positive thereafter. This pattern is consistent with an initial wait-and-see reduction in demand followed by slower expectation, defensive-purchase, and precautionary/speculative channels. Exchange-rate innovations produce the most persistent positive inflation response over the reported eight-quarter horizon. Because SMU is a derived composite uncertainty proxy and the VECM is not structurally identified, the estimates are interpreted as dynamic conditional associations rather than definitive causal effects. The findings therefore place monetary discipline and exchange-rate stability at the center of inflation control, while information transparency and credible communication remain complementary expectation-management instruments.

Highlights

• A Johansen–Juselius multivariate cointegration framework identifies two cointegrating relations among inflation, cyberspace uncertainty, real output, unemployment, liquidity, the exchange rate, and government size.

• A VECM is used consistently for long-run adjustment and short-run dynamics; generalized impulse responses show that exchange-rate shocks have the most persistent inflationary response over the reported horizon.

• The inflation response to a cyberspace-uncertainty shock is negative at short horizons but turns positive after roughly six quarters, suggesting a shift from an initial wait-and-see demand channel to delayed expectation and precautionary/speculative channels.

• A statistically significant error-correction adjustment and significant short-run effects of cyberspace uncertainty and liquidity confirm meaningful dynamics within the VECM framework.

Keywords
cyberspace uncertainty
inflation
Johansen cointegration
VECM
Iran
Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211–236. https://doi.org/10.1257/jep.31.2.211
Allcott, H., Gentzkow, M., & Yu, C. (2019). Trends in the diffusion of misinformation on social media. Research & Politics, 6(2), 1–8. https://doi.org/10.1177/2053168019848554
Babaie, M., Tavakolian, H., & Shakeri, A. (1397). Determinants of Inflation Forecast: A Dynamic Model Averaging Approach. Economics Research, 18(71), 261–311. https://doi.org/10.22054/joer.2018.9835 (In Persian).
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024
Baker, S. R., Bloom, N., Davis, S. J., & Renault, T. (2021). Twitter-Derived Measures of Economic Uncertainty. Working paper. Economic Policy Uncertainty project.
Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623–685. https://doi.org/10.3982/ECTA6248
Bloom, N. (2014). Fluctuations in uncertainty. Journal of Economic Perspectives, 28(2), 153–176. https://doi.org/10.1257/jep.28.2.153
Cinelli, M., Quattrociocchi, W., Galeazzi, A., et al. (2020). The COVID-19 social media infodemic. Scientific Reports, 10, 16598. https://doi.org/10.1038/s41598-020-73510-5
Coibion, O., Gorodnichenko, Y., & Kamdar, R. (2018). The formation of expectations, inflation, and the Phillips curve. Journal of Economic Literature, 56(4), 1447–1491. https://doi.org/10.1257/jel.20171300
Fernández-Villaverde, J., Guerrón-Quintana, P., Rubio-Ramírez, J. F., & Uribe, M. (2011). Risk matters: The real effects of volatility shocks. American Economic Review, 101(6), 2530–2561. https://doi.org/10.1257/aer.101.6.2530
Ha, J., Stocker, M. M., & Yilmazkuday, H. (2020). Inflation and exchange rate pass-through. Journal of International Money and Finance, 105, 102187. https://doi.org/10.1016/j.jimonfin.2020.102187
Jurado, K., Ludvigson, S. C., & Ng, S. (2015). Measuring uncertainty. Econometrica, 83(3), 1177–1216. https://doi.org/10.3982/ECTA11308
Laal Khezri, H., & Ashena, M. (1402). Investigating the Dynamic Relationship of Global Economic Policy Uncertainty with Inflation and Inflation Uncertainty in Iran. New Economy and Trade, 18(58), 149–171. https://doi.org/10.30465/jnet.2023.43351.1963 (In Persian).
Mankiw, N. G., & Reis, R. (2002). Sticky information versus sticky prices: A proposal to replace the New Keynesian Phillips curve. Quarterly Journal of Economics, 117(4), 1295–1328. https://doi.org/10.1162/003355302320935034
Rezazadeh, A. (1399). Relationship between inflation, inflation uncertainty and economic growth in Iran: Markov-switching approach. Journal of Iranian Economic Issues, 6(2), 37–66. https://doi.org/10.30465/ce.2020.5425 (In Persian).
Shalyari, F., Arman, S. A., & Salahmanesh, A. (1401). Investigating the Impact of Exchange Rate Uncertainty on Inflation and Interest Rate Uncertainty and the Role of Monetary and Fiscal Policy Credibility: Application of BEKK-VECH-VAR Methods. Journal of Applied Economics Studies in Iran, 11(43), 9–37. https://doi.org/10.22084/aes.2022.25294.3372 (In Persian).
Shiller, R. J. (2019). Narrative Economics: How Stories Go Viral and Drive Major Economic Events. Princeton University Press. https://doi.org/10.2307/j.ctvdf0jmq
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. https://doi.org/10.1126/science.aap9559
International Journal of New Political Economy

Articles in Press, Accepted Manuscript
Available Online from 08 September 2026

  • Receive Date 26 February 2026
  • Revise Date 02 September 2026
  • Accept Date 08 September 2026
  • First Publish Date 08 September 2026
  • Publish Date 08 September 2026