Neural Network-Based Numerical Analysis of the Impact of Pandemic Shocks in Three-Sector DSGE Model

Elisei Leonov ()
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Elisei Leonov: Gaidar Institute for Economic Policy; Institute of Applied Economic Research, RANEPA

Russian Journal of Money and Finance, 2023, vol. 82, issue 4, 80-107

Abstract: This paper focuses on numerical analysis of the impact of the pandemic shocks (lockdowns) on the economy based on a global solution and stochastic steady-state approximation of a three-sector model of the economy with immobile capital, irreversibility of investment, and installation costs. The impact of the lockdowns is analysed through the inclusion in the model of an exogenous restriction on the consumption of one sector, which is activated within a Markov process by the system's transition to a pandemic state. Given the significant difficulties in obtaining a global solution using traditional methods, a neural network-based approach is used. The results obtained show that the economy is sensitive to the nature of the expectations of pandemic shocks. In particular, pessimistic expectations lead to a drop in output and a decline in consumption in the long run.

Keywords: DSGE model; multi-sector model; global solution; neural networks; stochastic equilibrium; mode switching (search for similar items in EconPapers)
JEL-codes: C02 C61 C63 C65 E32 E37 E71 I10 L16 L17 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:bkr:journl:v:82:y:2023:i:4:p:80-107

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