Identifying Economic Shocks in a Rare Disaster Environment
Luisa Corrado,
Stefano Grassi () and
Aldo Paolillo ()
Additional contact information
Stefano Grassi: DEF and CEIS, Università di Roma "Tor Vergata", http://www.ceistorvergata.it
Aldo Paolillo: Università di Roma "Tor Vergata", http://www.ceistorvergata.it
No 517, CEIS Research Paper from Tor Vergata University, CEIS
Abstract:
We propose a new approach to efficiently estimate and analyze DSGE models subject to large shocks. The methodology is applied to study the macroeconomic effect of these unusual shocks in a new Two-Sector model with heterogenous exposure to the COVID-19 pandemic across sectors. We solve the model nonlinearly and propose a new nonlinear, non-Gaussian filter designed to handle large shocks and identify their source and time location. Monte Carlo experiments show that the estimation and identification of large shocks is feasible with a massively reduced running time. Empirical results indicate that the pandemic-induced economic downturn can be reconciled with a combination of large demand and supply shocks. Finally, we present a set of counterfactual experiments to filter out potential demand and supply shock complementarities, and perform a robustness exercise to check the sensitivity of the model parameters to large shocks.
Keywords: COVID-19; DSGE; Large shocks; Nonlinear; Non-Gaussian (search for similar items in EconPapers)
JEL-codes: C11 C51 E30 (search for similar items in EconPapers)
Pages: 75 pages
Date: 2021-10-15, Revised 2024-07-18
New Economics Papers: this item is included in nep-dge, nep-mac and nep-ore
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Citations: View citations in EconPapers (2)
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