Posted: Nov 02, 2023
The implementation of inflation targeting as a monetary policy framework has transformed central banking practices across both developed and emerging economies over the past three decades. While substantial literature exists examining the conventional macroeconomic outcomes of inflation targeting regimes, this research introduces a fundamentally novel computational approach that redefines how we understand the complex interplay between policy credibility, market microstructure, and institutional development. Traditional econometric analyses have predominantly focused on linear relationships and average treatment effects, potentially obscuring the critical threshold dynamics and adaptive learning processes that characterize modern financial systems. Our research breaks from this tradition by developing a quantum-inspired computational framework that captures the emergent properties of economic systems under inflation targeting regimes. This paper addresses a significant gap in the literature by examining how the computational complexity of market participants' decision-making processes mediates the effectiveness of inflation targeting policies. We propose that the conventional binary classification of countries as inflation targeters or non-targeters fails to capture the multidimensional nature of policy implementation and market response. Instead, we conceptualize inflation targeting as a complex adaptive system where policy signals interact with heterogeneous agents possessing varying computational capabilities to process information. This perspective allows us to investigate previously unexplored questions about how digital transformation and artificial intelligence in financial markets are altering the traditional transmission mechanisms of monetary policy.
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