CREATES

Modelling and Estimating Large Macroeconomic Shocks During the Pandemic

Publikation: Working paper/Preprint Working paperForskning

Standard

Modelling and Estimating Large Macroeconomic Shocks During the Pandemic. / Corrado, Luisa; Grassi, Stefano; Paolillo, Aldo.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2021.

Publikation: Working paper/Preprint Working paperForskning

Harvard

Corrado, L, Grassi, S & Paolillo, A 2021 'Modelling and Estimating Large Macroeconomic Shocks During the Pandemic' Institut for Økonomi, Aarhus Universitet, Aarhus.

APA

Corrado, L., Grassi, S., & Paolillo, A. (2021). Modelling and Estimating Large Macroeconomic Shocks During the Pandemic. Institut for Økonomi, Aarhus Universitet. CREATES Research Papers Nr. 2021-08

CBE

Corrado L, Grassi S, Paolillo A. 2021. Modelling and Estimating Large Macroeconomic Shocks During the Pandemic. Aarhus: Institut for Økonomi, Aarhus Universitet.

MLA

Corrado, Luisa, Stefano Grassi og Aldo Paolillo Modelling and Estimating Large Macroeconomic Shocks During the Pandemic. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal nr. 2021-08). 2021., 57 s.

Vancouver

Corrado L, Grassi S, Paolillo A. Modelling and Estimating Large Macroeconomic Shocks During the Pandemic. Aarhus: Institut for Økonomi, Aarhus Universitet. 2021 jun 15.

Author

Corrado, Luisa ; Grassi, Stefano ; Paolillo, Aldo. / Modelling and Estimating Large Macroeconomic Shocks During the Pandemic. Aarhus : Institut for Økonomi, Aarhus Universitet, 2021. (CREATES Research Papers; Nr. 2021-08).

Bibtex

@techreport{bfe0e2bef6cb41589c8a312cee741285,
title = "Modelling and Estimating Large Macroeconomic Shocks During the Pandemic",
abstract = "This paper proposes and estimates a new Two-Sector One-Agent model that features large shocks. The resulting medium-scale New Keynesian model includes the standard real and nominal frictions used in the empirical literature and allows for heterogeneous COVID-19 pandemic exposure across sectors. We solve the model nonlinearly and we propose a new nonlinear, non-Gaussian filter designed to handle large pandemic shocks to make inference feasible. Monte Carlo experiments show that it correctly identifies the source and time location of shocks with a massively reduced running time, making the estimation of macro-models with disaster shocks feasible. The estimation is carried out using the Sequential Monte Carlo sampler recently proposed by Herbst and Schorfheide (2014). Our empirical results show that the pandemic-induced economic downturn can be reconciled with a combination of large demand and supply shocks. More precisely, starting from the second quarter of 2020, the model detects the occurrence of a large negative demand shock in consuming all kinds of goods, together with a large negative demand shock in consuming contact-intensive products. On the supply side, our proposed method detects a large labor supply shock to the general sector and a large labor productivity shock in the pandemic-sensitive sector.",
keywords = "COVID-19, Nonlinear, Non-Gaussian, Large shocks, DSGE",
author = "Luisa Corrado and Stefano Grassi and Aldo Paolillo",
year = "2021",
month = jun,
day = "15",
language = "English",
series = "CREATES Research Papers",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
number = "2021-08",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Modelling and Estimating Large Macroeconomic Shocks During the Pandemic

AU - Corrado, Luisa

AU - Grassi, Stefano

AU - Paolillo, Aldo

PY - 2021/6/15

Y1 - 2021/6/15

N2 - This paper proposes and estimates a new Two-Sector One-Agent model that features large shocks. The resulting medium-scale New Keynesian model includes the standard real and nominal frictions used in the empirical literature and allows for heterogeneous COVID-19 pandemic exposure across sectors. We solve the model nonlinearly and we propose a new nonlinear, non-Gaussian filter designed to handle large pandemic shocks to make inference feasible. Monte Carlo experiments show that it correctly identifies the source and time location of shocks with a massively reduced running time, making the estimation of macro-models with disaster shocks feasible. The estimation is carried out using the Sequential Monte Carlo sampler recently proposed by Herbst and Schorfheide (2014). Our empirical results show that the pandemic-induced economic downturn can be reconciled with a combination of large demand and supply shocks. More precisely, starting from the second quarter of 2020, the model detects the occurrence of a large negative demand shock in consuming all kinds of goods, together with a large negative demand shock in consuming contact-intensive products. On the supply side, our proposed method detects a large labor supply shock to the general sector and a large labor productivity shock in the pandemic-sensitive sector.

AB - This paper proposes and estimates a new Two-Sector One-Agent model that features large shocks. The resulting medium-scale New Keynesian model includes the standard real and nominal frictions used in the empirical literature and allows for heterogeneous COVID-19 pandemic exposure across sectors. We solve the model nonlinearly and we propose a new nonlinear, non-Gaussian filter designed to handle large pandemic shocks to make inference feasible. Monte Carlo experiments show that it correctly identifies the source and time location of shocks with a massively reduced running time, making the estimation of macro-models with disaster shocks feasible. The estimation is carried out using the Sequential Monte Carlo sampler recently proposed by Herbst and Schorfheide (2014). Our empirical results show that the pandemic-induced economic downturn can be reconciled with a combination of large demand and supply shocks. More precisely, starting from the second quarter of 2020, the model detects the occurrence of a large negative demand shock in consuming all kinds of goods, together with a large negative demand shock in consuming contact-intensive products. On the supply side, our proposed method detects a large labor supply shock to the general sector and a large labor productivity shock in the pandemic-sensitive sector.

KW - COVID-19

KW - Nonlinear

KW - Non-Gaussian

KW - Large shocks

KW - DSGE

M3 - Working paper

T3 - CREATES Research Papers

BT - Modelling and Estimating Large Macroeconomic Shocks During the Pandemic

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -