Learning to Live in a Liquidity Trap

32 Pages Posted: 21 Aug 2017

See all articles by Jasmina Arifovic

Jasmina Arifovic

Simon Fraser University (SFU) - Department of Economics

Stephanie Schmitt-Grohé

Centre for Economic Policy Research (CEPR); National Bureau of Economic Research (NBER)

Martín Uribe

Columbia University - Graduate School of Arts and Sciences - Department of Economics; National Bureau of Economic Research (NBER)

Date Written: August 2017

Abstract

The Taylor rule in combination with the zero lower bound on nominal rates has been shown to create an unintended liquidity-trap equilibrium. The relevance of this equilibrium has been challenged on the basis that it is not stable under least-square learning. In this paper, we show that the liquidity-trap equilibrium is stable under social learning. The learning mechanism we employ includes three realistic elements: mutation, crossover, and tournaments. We show that agents can learn to have pessimistic sentiments about the central bank's ability to generate price growth, giving rise to a stochastically stable environment characterized by deflation and stagnation.

Suggested Citation

Arifovic, Jasmina and Schmitt-Grohe, Stephanie and Uribe, Martin, Learning to Live in a Liquidity Trap (August 2017). NBER Working Paper No. w23725, Available at SSRN: https://ssrn.com/abstract=3023123

Jasmina Arifovic (Contact Author)

Simon Fraser University (SFU) - Department of Economics ( email )

8888 University Drive
Burnaby, British Columbia V5A 1S6
Canada
604-291-3508 (Phone)
604-291-5944 (Fax)

Stephanie Schmitt-Grohe

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Martin Uribe

Columbia University - Graduate School of Arts and Sciences - Department of Economics ( email )

420 W. 118th Street
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New York, NY 10027
United States
212-851-4008 (Phone)
212-854-8059 (Fax)

National Bureau of Economic Research (NBER)

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Cambridge, MA 02138
United States

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