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To freeze or not to freeze? Epidemic prevention and control in the DSGE model with agent-based epidemic component

dc.contributor.authorKaszowska-Mojsa, Jagoda
dc.contributor.authorWłodarczyk, Przemysław
dc.date.accessioned2020-11-12T08:24:29Z
dc.date.available2020-11-12T08:24:29Z
dc.date.issued2020-11-10
dc.identifier.urihttp://hdl.handle.net/11089/32520
dc.description.abstractThe ongoing epidemic of COVID-19 raises numerous questions concerning the shape and range of state interventions, that are aimed at reduction of the number of infections and deaths. The lockdowns, which became the most popular response worldwide, are assessed as being an outdated and economically inefficient way to fight the disease. However, in the absence of efficient cures and vaccines they lack viable alternatives. In this paper we assess the economic consequences of epidemic prevention and control schemes that were introduced in order to respond to the COVID-19 outburst. The analyses report the results of epidemic simulations obtained with the agent-based modeling methods under different response schemes and use them in order to provide conditional forecasts of standard economic variables. The forecasts are obtained from the DSGE model with labour market component.pl_PL
dc.language.isoenpl_PL
dc.relation.ispartofseriesLodz Economics Working Papers;3
dc.subjectCOVID-19pl_PL
dc.subjectagent-based modellingpl_PL
dc.subjectdynamic stochastic general equilibrium modelspl_PL
dc.subjectscenario analysispl_PL
dc.titleTo freeze or not to freeze? Epidemic prevention and control in the DSGE model with agent-based epidemic componentpl_PL
dc.typeWorking Paperpl_PL
dc.page.number1-32pl_PL
dc.contributor.authorAffiliationInstitute of Economics Polish Academy of Sciencespl_PL
dc.contributor.authorAffiliationUniversity of Lodz, Faculty of Economics and Sociology, Department of Macroeconomicspl_PL
dc.referencesHunter, E.; Mac Nameeb, B.; Kellehera, J. A Taxonomy for Agent-Based Models in Human Infectious Disease Epidemiology, J. Artif. Soc. 2017, 20, 3. doi: 10.18564/jasss.3414pl_PL
dc.referencesAjelli, M.; Gonçalves, B.; Balcan, D.; Colizza, V.; Hu, H.; Ramasco, J.; Merler, S.; Vespignani, A. Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models. BMC Infect Dis 2010, 10, 190. doi: 10.1186/1471-2334-10-190pl_PL
dc.referencesDunham, J. An agent-based spatially explicit epidemiological model in MASON. Journal of Artificial Societies and Social Simulation 2005, 9, 1.pl_PL
dc.referencesEichenbaum, M.; Rebelo, S.; Trabandt, M. The Macroeconomics of Epidemics. NBER WP 2020, 26882, 1–35. doi: 10.3386/w26882pl_PL
dc.referencesMihailov, A. Quantifying the Macroeconomic Effects of the COVID 19 Lockdown: Comparative Simulations of the Estimated Galí Smets Wouters Model. Univ. of Reading Disc. Paper 2020, 2020-07, 1–32pl_PL
dc.referencesYanga, Y.; Zhangb,H.; Chenc, X. Coronavirus pandemic and tourism: Dynamic stochastic general equilibrium modeling of infectious disease outbreak. Ann Tour Res. 2020, 83, 2–6. doi: 10.1016/j.annals.2020.102913pl_PL
dc.referencesToda, A. Susceptible-Infected-Recovered(SIR) Dynamics of Covid-19 and Economic Impact, Covid Economics, Vetted and Real-Time Papers (CEPR) 2020, 1, 1–15.pl_PL
dc.referencesJordà, O.; Singh, S.R.; Taylor, A.M. Longer-run Economic Consequences of Pandemics, NBER WP 2020, 26934, 3–20. doi: 10.3386/w26934pl_PL
dc.referencesGuerrieri, V.; Lorenzoni, G.; Straub, L.; Werning, I.; Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages?, NBER WP 2020, 26918. doi: 10.3386/w26918pl_PL
dc.referencesGuerrieri, V.; Lorenzoni, G.; Straub, L.; Werning, I.; Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages?, NBER WP 2020, 26918. doi: 10.3386/w2691pl_PL
dc.referencesCuevas, E. An agent-based model to evaluate the COVID-19 transmission risks in facilities, Comput Biol Med. 2020, 121, 1–12. doi: 10.1016/j.compbiomed.2020.103827pl_PL
dc.referencesSilva, P.; Batista, P.; Lima, H.; Alves, M.; Guimarães, F.; Silva, R. COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions, Chaos Soliton Fract 2020, 139, 1–12. doi: 10.1016/j.chaos.2020.110088pl_PL
dc.referencesShamil, M.S.; Farheen, F.; Ibtehaz, N., Khan, I.; Rahman, M. An Agent Based Modeling of COVID-19: Validation, Analysis and Recommendations. medRxiv Preprints 2020, 1–17, doi: 10.1101/2020.07.05.20146977pl_PL
dc.referencesHoertel, N.; Blachier, M.; Blanco, C.; Olfson, M.; Massetti, M.; Sánchez Rico, M., Limosin, F., Leleu, H. A stochastic agent-based model of the SARS-CoV-2 epidemic in France. Nat Med 2020 26, 1417–1421. doi:10.1038/s41591-020-1001-6pl_PL
dc.referencesWallentin, G., Kaziyeva, D., Reibersdorfer-Adelsberger, E. COVID-19 Intervention Scenarios for a Long-term Disease Management. Int. J. Health Policy Manag. 2020, In press, 1–9. doi: 10.34172/ijhpm.2020.130pl_PL
dc.referencesCurrie, Ch.; Fowler, J.; Kotiadis, K.; Monks, T.; Onggo, B.; Robertson, D.; Tako, A. How simulation modelling can help reduce the impact of COVID-19, J. Simul 2020, 14, 83–97. doi 10.1080/17477778.2020.1751570pl_PL
dc.referencesBertozzi, A.; Franco, E.; Mohler, G.; Short, M.; Sledge, D. The challenges of modeling and forecasting the spread of COVID-19. Proc Natl Acad Sci U S A 2020, 117, 16732–16738. doi: 10.1073/pnas.200652011pl_PL
dc.referencesKlôh, V.; Silva, G.; Ferro, M.; Araújo, E.; Barros de Melo, C.; Pinho de Andrade Lima, J.R; Rademaker Martins, E.The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro. Braz J of Hea Rev 2020, 3, 647–3673. doi: 10.34119/bjhrv3n2-192pl_PL
dc.referencesMaziarz, M.; Zach, M.. Agent-based modelling for SARS-CoV-2 epidemic prediction and intervention assessment: A methodological appraisal. J. Eval. Clin. 2020, 26, 1352–1360. doi: 10.1111/jep.13459pl_PL
dc.referencesKano, T.; Yasui, K.; Mikami, T.; Asally, M.; Ishiguro, A. An agent-based model for interrelation between COVID-19 outbreak and economic activities. Available online: https://arxiv.org/abs/2007.11988 (accessed on 29 October 2020)pl_PL
dc.referencesBrotherhood, L., Kircher, P., Santos, C., Tertilt, M. An Economic Model of the COVID-19 Epidemic: The Importance of Testing and Age-Specific Policies, IZA Discussion Paper 2020, 13265, 1–68.pl_PL
dc.referencesBrottier, I. COVID 19: The Good, the Bad and the Agent Based Model. Available online: https://www.anylogic.com/blog/covid-19-the-good-the-bad-and-the-agent-based-model/ (accessed on 29 October 2020).pl_PL
dc.referencesAdam, D. Special report: The simulations driving the world’s response to COVID-19. How epidemiologists rushed to model the coronavirus pandemic. Available online: https://www.nature.com/articles/d41586-020-01003-6 (accessed on 29 October 2020).pl_PL
dc.referencesWolfram, Ch. Agent-Based Network Models for COVID-19. Available online: https://www.wolframcloud.com/obj/covid-19/Published/Agent-Based-Networks-Models-for-COVID-19.nb (accessed on 20 October 2020).pl_PL
dc.referencesGalí, J. Monetary policy, inflation, and the business cycle. An introduction to the New Keynesian framework, 1st ed.; Princeton University Press: Princeton, US, 2008; pp. 3–224.pl_PL
dc.referencesChristiano, L.; Eichenbaum, M.; Evans, C. Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy. J Polit Econ 2005, 113, 1–45.pl_PL
dc.referencesGalí, J. Unemployment Fluctuations and Stabilization Policies. A New Keynesian Perspective, 1st ed.; MIT Press: Cambridge, US, 2011; pp. 2–120.pl_PL
dc.referencesGalí, J. Monetary Policy, Inflation, and the Business Cycle. An Introduction to the New Keynesian Framework and Its Applications, 1st ed.; Princeton University Press: Princeton, US, 2015; pp. 2–224.pl_PL
dc.referencesGalí, J.; Smets, F.; Wouters, R. Unemployment in an Estimated New Keynesian Model. In NBER Macroeconomics Annual 2011, 1st ed.; Acemoglu, D.; Woodford, M., Eds.; Chicago University Press: Chicago, US, 2012; 26, pp. 329–360pl_PL
dc.referencesKing, R.G.; Plosser, C.I.; Rebelo, S.T. Production, growth and business cycles: I. The basic neoclassical model J. Monet. Econ. 1988, 21, 195–232. doi: 10.1016/0304-3932(88)90030-Xpl_PL
dc.referencesSmets, F.; Wouters, R. Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach. Am Econ Rev 2007, 97, 586–606. doi: 10.1257/aer.97.3.586pl_PL
dc.referencesAbel, A. Asset Prices under Habit Formation and Catching up with the Jonses Am Econ Rev 1990, 80, 38–42.pl_PL
dc.referencesChristiano, L.; Trabandt, M.; Walentin, K. Involuntary Unemployment and the Business Cycle. Rev. Econ. Dyn 2020, In press. doi: 10.1016/j.red.2020.05.003pl_PL
dc.referencesCalvo, G. Staggered prices in a utility-maximising framework J. Monet. Econ. 1983, 12, 383–398. doi: 10.1016/0304-3932(83)90060-0pl_PL
dc.referencesMerz, M. Search in the labor market and the real business cycle J. Monet. Econ. 1995, 36, 269–300. doi: 10.1016/0304-3932(95)01216-8pl_PL
dc.referencesArmington, P. A Theory of Demand for Products Distinguished by Place of Production. IMF Staff Papers 1969, 16, pp. 159–178. doi: 10.2307/3866403pl_PL
dc.referencesGreen, H. Aggregation in Economic Analysis, 1st ed.; Princeton University Press: Princeton, US, 1964; pp. 2–140.pl_PL
dc.referencesDixit, A. & Stiglitz, J. Monopolistic Competition and Optimum Product Diversity. Am Econ Rev 1977, 67, 297–308. doi: 10.7916/D8S75S91pl_PL
dc.referencesChristiano, L.; Trabandt, M.; Walentin, K. DSGE Models for Monetary Policy Analysis. In Handbooks in Economics. Monetary Economics, 1st ed.; Friedman, B., Woodford, M., Eds.; North-Holland: Amsterdam, Netherlands, 2011; Volume 3A, pp. 285–367.pl_PL
dc.contributor.authorEmailjagoda.kaszowska@inepan.waw.plpl_PL
dc.contributor.authorEmailprzemyslaw.wlodarczyk@uni.lodz.plpl_PL
dc.disciplineekonomia i finansepl_PL


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