24/11/2025
📣Special Seminar
Title: Dynamic Inflation Forecasting with Fuzzy Inferred Non-stationary PCA and Hierarchic GBM
Speaker: Res. Asst. Dr. Oğuz Koç
Department: Financial Mathematics
Place: IAM Seminar Room
Date/Time: November 27, 2025 / 15.00
Presented at: Workshop “2025 9th International Conference on Applied Economics and Business”, B&B HOTEL, Paris, France
Abstract:
Accurate inflation forecasting is essential for policymakers, businesses, and households because it shapes budgeting, monetary policy decisions, investment planning, wage setting, and overall economic stability. Yet inflation is difficult to predict due to the combined effects of monetary and fiscal policies, global conditions, shocks, and geopolitical risks. This study proposes a dynamic multinomial framework designed to capture both the underlying structure of macroeconomic data and the evolving regimes that drive inflation dynamics. The model integrates fuzzy logic with principal component analysis to create Fuzzy-Inferred PCA (FIPCA), a dimensionality reduction method that represents uncertainty and nonlinear interactions among macroeconomic variables. This produces a condensed time series that more effectively summarizes the broader economic environment compared to traditional PCA.
This macroeconomic indicator is then used within a Hierarchical Hidden Markov Model that identifies long-term economic regimes and produces state probabilities reflecting shifts in inflation behavior. These regimes guide a stochastic process based on Geometric Brownian Motion, allowing inflation paths to evolve according to changing economic conditions rather than fixed parameters. Regime-specific parameters are estimated using data subsets aligned with the identified states, providing a flexible structure that adapts to transitions in the economy. The resulting hybrid framework demonstrates that combining fuzzy-enhanced dimensionality reduction, regime identification, and state-dependent stochastic modeling can improve the accuracy and reliability of inflation forecasting in environments subject to structural change and uncertainty.
Joint work with: A. Sevtap Selcuk-Kestel