TY  - UNPD
A1  - Tänzer, Alina
T1  - The effectiveness of central Bank purchases of long-term treasury securities: a neural network approach
N2  - Central bank intervention in the form of quantitative easing (QE) during times of low interest rates is a controversial topic. The author 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.
T3  - Working paper series / Institute for Monetary and Financial Stability - 204 
KW  - Artificial Intelligence
KW  - Machine Learning
KW  - Neural Networks
KW  - Forecasting and Simulation
KW  - Models and Applications
KW  - Financial Markets and the Macroeconomy
KW  - Monetary Policy
KW  - Central Banks and Their Policies
Y1  - 2024
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/80162
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-801620
UR  - https://www.imfs-frankfurt.de/forschung/imfs-working-papers/details.html?tx_mmpublications_publicationsdetail%5Bcontroller%5D=Publication&tx_mmpublications_publicationsdetail%5Bpublication%5D=480&cHash=cfa3675efa199c123471a495b751dc4d
PB  - Johann Wolfgang Goethe-Univ., Inst. for Monetary and Financial Stability
CY  - Frankfurt am Main
ER  -