The effectiveness of central bank purchases of long-term treasury securities: A neural network approach

Alina Tänzer

No 204, IMFS Working Paper Series from Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS)

Abstract: 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: Artificial Intelligence; Machine Learning; Neural Networks; Forecasting and Simulation: Models and Applications; Financial Markets and the Macroeconomy; Monetary Policy; Central Banks and Their Policies (search for similar items in EconPapers)
JEL-codes: C45 E44 E47 E52 E58 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-ain, nep-ban, nep-big, nep-cba, nep-cmp, nep-mac and nep-mon
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:imfswp:295732

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