A composite approach to nonlinear inflation dynamics in BRICS countries and Türkiye
Yusifzada, Tural; Comert, Hasan; Ahmadov, Vugar (29.07.2025)
Numero
5/2025Julkaisija
Bank of Finland
2025
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2025072979800Tiivistelmä
This study introduces a novel composite approach to nonlinear inflation dynamics in identifying historical inflation patterns and forecasting future regime shifts. Assuming inflation’s responsiveness to its determinants varies across inflation regimes and that inflation shock magnitude shapes the dynamics, we endogenously identify distinct inflation regimes and analyze nonlinear behaviors within such regimes for the BRICS countries (Brazil, Russia, India, China, and South Africa) and Türkiye. In the first stage of our analysis, we employ a Hidden Markov Regime Switching Model combined with Monte Carlo simulations to establish high- and low-inflation thresholds. In the second stage, we utilize an ordered probit model to identify nonlinear probabilistic relationships between inflation regimes and key drivers of inflation such as unit labor costs, exchange rates, and global inflation. Our method achieves over 90% accuracy in predicting inflation regimes based on historical data. It also shows particularly strong out-of-sample performance in the post-pandemic period, outperforming the forecasts of international financial institutions. Even without prior knowledge of exogenous variables, the method anticipates regime shifts in five of the six countries analyzed for 2022 and 2023. Our approach offers researchers and central bankers a robust alternative analytical framework for managing high- and low-inflation environments where traditional linear or equilibrium-based models fall short.
Julkaisuhuomautus
NON-TECHNICAL SUMMARY
FOCUS
The study investigates the nonlinear inflation dynamics in emerging economies, with a particular focus on the BRICS countries (Brazil, Russia, India, China, and South Africa) and Türkiye. We explore how the relationship between inflation and its underlying determinants evolves across different inflation regimes and in response to varying shock magnitudes. Specifically, we analyze the structural behavior of both high- and low-inflation environments, assessing how regime-dependent and magnitude-driven nonlinearities affect inflation forecasting and detection of regime transitions in these economies.
CONTRIBUTION
The study introduces a novel composite framework that integrates shock magnitude and regime-based nonlinearities. Using a Hidden Markov Regime Switching Model (HMM) with Monte Carlo simulations, we endogenously identify inflation regimes for each country from the 1990s to 2024. We also develop a probit model that incorporates magnitude-related nonlinearities to predict inflation regimes accurately. The framework enhances theoretical understanding of inflation dynamics in emerging markets and can serve as an early-warning tool for monetary authorities facing inflationary risks in uncertain environments.
FINDINGS
Our composite model achieves over 90% in-sample forecasting accuracy and demonstrates robust out-of-sample predictions for post-pandemic inflation dynamics in the BRICS countries and Türkiye. The results indicate that inflation exhibits an asymmetric response to large shocks. Pricing behavior varies across inflation regimes, with cautious adjustments prevailing in low-inflation regimes, and aggressive, synchronized price increases characterizing high-inflation regimes. The framework accurately detects regime shifts such as the post-2020 surge in inflation without relying on exogenous inputs, thereby underscoring its practical utility for monetary policy in emerging economies.
FOCUS
The study investigates the nonlinear inflation dynamics in emerging economies, with a particular focus on the BRICS countries (Brazil, Russia, India, China, and South Africa) and Türkiye. We explore how the relationship between inflation and its underlying determinants evolves across different inflation regimes and in response to varying shock magnitudes. Specifically, we analyze the structural behavior of both high- and low-inflation environments, assessing how regime-dependent and magnitude-driven nonlinearities affect inflation forecasting and detection of regime transitions in these economies.
CONTRIBUTION
The study introduces a novel composite framework that integrates shock magnitude and regime-based nonlinearities. Using a Hidden Markov Regime Switching Model (HMM) with Monte Carlo simulations, we endogenously identify inflation regimes for each country from the 1990s to 2024. We also develop a probit model that incorporates magnitude-related nonlinearities to predict inflation regimes accurately. The framework enhances theoretical understanding of inflation dynamics in emerging markets and can serve as an early-warning tool for monetary authorities facing inflationary risks in uncertain environments.
FINDINGS
Our composite model achieves over 90% in-sample forecasting accuracy and demonstrates robust out-of-sample predictions for post-pandemic inflation dynamics in the BRICS countries and Türkiye. The results indicate that inflation exhibits an asymmetric response to large shocks. Pricing behavior varies across inflation regimes, with cautious adjustments prevailing in low-inflation regimes, and aggressive, synchronized price increases characterizing high-inflation regimes. The framework accurately detects regime shifts such as the post-2020 surge in inflation without relying on exogenous inputs, thereby underscoring its practical utility for monetary policy in emerging economies.