Discussion Paper Details

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Title: When the U.S. catches a cold, Canada sneezes: a lower-bound tale told by deep learning

Author(s): Vadym Lepetyuk, Lilia Maliar and Serguei Maliar

Publication Date: September 2019

Keyword(s): central banking, clustering analysis large-scale model, deep learning, Machine Learning, neural networks, New Keynesian Model, supervised learning, ToTEM and ZLB

Programme Area(s): Monetary Economics and Fluctuations

Abstract: The Canadian economy was not initially hit by the 2007-2009 Great Recession but ended up having a prolonged episode of the effective lower bound (ELB) on nominal interest rates. To investigate the Canadian ELB experience, we build a "baby" ToTEM model -- a scaled-down version of the Terms of Trade Economic Model (ToTEM) of the Bank of Canada. Our model includes 49 nonlinear equations and 21 state variables. To solve such a high-dimensional model, we develop a projection deep learning algorithm -- a combination of unsupervised and supervised (deep) machine learning techniques. Our findings are as follows: The Canadian ELB episode was contaminated from abroad via large foreign demand shocks. Prolonged ELB episodes are easy to generate with foreign shocks, unlike with domestic shocks. Nonlinearities associated with the ELB constraint have virtually no impact on the Canadian economy but other nonlinearities do, in particular, the degree of uncertainty and specific closing condition used to induce the model's stationarity.

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Bibliographic Reference

Lepetyuk, V, Maliar, L and Maliar, S. 2019. 'When the U.S. catches a cold, Canada sneezes: a lower-bound tale told by deep learning'. London, Centre for Economic Policy Research.