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

Author

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  • Elisei Leonov

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

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.

Suggested Citation

  • Elisei Leonov, 2023. "Neural Network-Based Numerical Analysis of the Impact of Pandemic Shocks in Three-Sector DSGE Model," Russian Journal of Money and Finance, Bank of Russia, vol. 82(4), pages 80-107, December.
    • Handle: RePEc:bkr:journl:v:82:y:2023:i:4:p:80-107
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    References listed on IDEAS

    as
    1. Marlon Azinovic & Luca Gaegauf & Simon Scheidegger, 2022. "Deep Equilibrium Nets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1471-1525, November.
    2. Abel Brodeur & David Gray & Anik Islam & Suraiya Bhuiyan, 2021. "A literature review of the economics of COVID‐19," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1007-1044, September.
    3. Martin Henseler & Hélène Maisonnave & Asiya Maskaeva, 2021. "Economic impacts of COVID-19 on the tourism sector in Tanzania," Working Papers hal-03501722, HAL.
    4. Paul Levine & Neil Rickman, 2021. "Optimal Lockdown in an Epidemiological-Macroeconomic Model," School of Economics Discussion Papers 0421, School of Economics, University of Surrey.
    5. Maliar, Lilia & Maliar, Serguei & Winant, Pablo, 2021. "Deep learning for solving dynamic economic models," Journal of Monetary Economics, Elsevier, vol. 122(C), pages 76-101.
    6. Yang, Yang & Zhang, Hongru & Chen, Xiang, 2020. "Coronavirus pandemic and tourism: Dynamic stochastic general equilibrium modeling of infectious disease outbreak," Annals of Tourism Research, Elsevier, vol. 83(C).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    DSGE model; multi-sector model; global solution; neural networks; stochastic equilibrium; mode switching;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E71 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on the Macro Economy
    • I10 - Health, Education, and Welfare - - Health - - - General
    • L16 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Industrial Organization and Macroeconomics; Macroeconomic Industrial Structure
    • L17 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Open Source Products and Markets

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