When the U.S. catches a cold, Canada sneezes: a lower-bound tale told by deep learning
Serguei Maliar, Lilia Maliar, and Vadym Lepetyuk
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.