Adaptive Learning in Macroeconomic Models - Understanding Monetary Policy
This research project considers adaptive learning algorithms for estimating the Taylor rule to strengthen the public sector's understanding of monetary policy. Unit root tests that allow for a structural break are conducted to analyse the stationarity assumption for macroeconomic time series. In the first step, the Kalman filter simulates economic agents' expectations concerning inflation and unemployment rates. Maximum likelihood optimisations and Monte Carlo integrations accompany the estimation of additional parameters and investigate the statistical significance of coefficients. Afterwards, a constant-gain least squares algorithm successfully estimates the policy reaction function in the United States. The results suggest that the inertial character of interest rates increased significantly during the observation period and that the Fed started to control inflationary pressure adequately in 1984.