The effectiveness of central bank purchases of long-term treasury securities: A neural network approach - Alina Tänzer (Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), 2024)
Central bank intervention in the form of quantitative easing (QE) during times of low interest rates is a controversial topic. This paper introduces a novel approach to study the effectiveness of such unconventional measures. Using U.S. data on six key financial and macroeconomic variables between 1990 and 2015, the economy is estimated by artificial neural networks. Historical counterfactual analyses show that real effects are less pronounced than yield effects. Disentangling the effects of the individual asset purchase programs, impulse response functions provide evidence for QE being less effective the more the crisis is overcome. The peak effects of all QE interventions during the Financial Crisis only amounts to 1.3 pp for GDP growth and 0.6 pp for inflation respectively. Hence, the time as well as the volume of the interventions should be deliberated.
Keywords: C45; E47; E44; E52; E58; 330; Artificial Intelligence; Machine Learning; Neural Networks; Forecasting and Simulation: Models and Applications; Financial Markets and the Macroeconomy; Monetary Policy; Central Banks and Their Policies