Research Project

Research Projects

My research explores the interaction between monetary policy, financial markets, and macroeconomic dynamics using advanced econometric and computational techniques. I am particularly interested in understanding how changes in monetary policy regimes influence interest rates, yield curve behavior, and financial market outcomes in emerging economies.

My work combines traditional econometric approaches with modern data-driven methods, including Markov Switching Vector Autoregression (MS-VAR), Dynamic Nelson–Siegel yield curve modeling, time-series econometrics, and machine learning techniques such as Long Short-Term Memory (LSTM) networks. Through these methodologies, I investigate regime-dependent monetary policy transmission, interest rate forecasting, and the evolution of financial market dynamics.

The primary objective of my research is to provide empirical evidence that can support policymakers, financial institutions, and researchers in understanding complex economic relationships and improving policy effectiveness. My ongoing projects contribute to the fields of monetary economics, financial econometrics, international finance, and applied economic policy analysis.

Current Research Projects: 

Artificial Intelligence and Monetary Policy: Identifying Regimes Using LSTM Models 

This research project explores the application of Long Short-Term Memory (LSTM) neural networks to identify and classify monetary policy regimes using macroeconomic and financial market data. Traditional regime identification methods often rely on econometric techniques such as Markov switching models, which require predefined assumptions regarding regime transitions. In contrast, LSTM models can capture complex nonlinear relationships and temporal dependencies within economic data, potentially improving the detection of regime changes.

 The study investigates whether machine learning techniques can effectively distinguish between accommodative, neutral, and restrictive monetary policy regimes by analyzing key economic indicators, interest rates, inflation, and financial market variables. The findings aim to contribute to the growing literature on artificial intelligence in economics and provide new tools for monetary policy analysis, forecasting, and decision-making.

 Keywords: LSTM, machine learning, monetary policy regimes, artificial intelligence, macroeconomic forecasting, financial econometrics, time-series analysis.