Modeling Brainard’s Uncertainty within a Structural Shock Framework and Analyzing Dynamic Responses
Keywords:
Brainard Uncertainty, Banking Stability, Economic Growth, DSGE Model, Structural Shocks, Bayesian EstimationAbstract
This study aims to examine the long-run effects of Brainard uncertainty and banking stability on Iran’s economic growth within a DSGE framework. This applied causal-analytical study uses financial data from banks listed on the Tehran Stock Exchange over 1997–2017. Brainard uncertainty is incorporated as a multiplicative structural shock in a DSGE model, while banking stability is measured using Altman’s Z-score. Monetary policy volatility is modeled using ARCH-family models, and long-run and dynamic relationships are estimated through Bayesian methods employing MCMC algorithms and particle filtering. Control variables include physical capital and labor, and all variables are log-transformed and detrended using the Hodrick–Prescott filter . Results indicate that Brainard uncertainty has a statistically significant negative effect on output and investment, leading to a persistent decline in economic growth. In contrast, banking stability shows a significant positive impact on growth. The velocity of money is positively associated with real GDP, inflation, and exchange rate, whereas interest rates have a significant negative effect. Household consumption does not exhibit statistical significance. The findings suggest that reducing policy uncertainty and enhancing banking stability are critical for sustainable economic growth, and rule-based and transparent policymaking improves monetary policy effectiveness.
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Copyright (c) 2025 Massod Emami (Author); Roya Al Emran (Corresponding author); Sima Eskandari Sabzi (Translator)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
