When the U.S. catches a cold, Canada sneezes: A lower-bound tale told by deep learning
Lilia Maliar and
Journal of Economic Dynamics and Control, 2020, vol. 117, issue C
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 the 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.
Keywords: Central banking; Policymakers; ToTEM; bToTEM; Stochastic simulation; Machine learning; Deep learning; Supervised learning; Unsupervised learning; Neural networks; Ergodic set; Clustering analysis; Adaptive grid; Central bank; Bank of Canada; US Fed; Large-scale model; New Keynesian model; ZLB; ELB (search for similar items in EconPapers)
JEL-codes: C61 C63 C68 E31 E52 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:117:y:2020:i:c:s0165188920300944
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Journal of Economic Dynamics and Control is currently edited by J. Bullard, C. Chiarella, H. Dawid, C. H. Hommes, P. Klein and C. Otrok
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