Forecasting the propagation of pandemic shocks with a dynamic input-output model

Anton Pichler; Marco Pangallo; R. Maria del Rio-Chanona; François Lafond; J. Doyne Farmer

doi:10.1016/j.jedc.2022.104527

Forecasting the propagation of pandemic shocks with a dynamic input-output model

Anton Pichler^*, Marco Pangallo, R. Maria del Rio-Chanona, François Lafond, J. Doyne Farmer

^*Corresponding author for this work

Publication: Scientific journal › Journal article › peer-review

Abstract

We introduce a dynamic disequilibrium input-output model that was used to forecast the economics of the COVID-19 pandemic. This model was designed to understand the upstream and downstream propagation of the industry-specific demand and supply shocks caused by COVID-19, which were exceptional in their severity, suddenness and heterogeneity across industries. The model, which was inspired in part by previous work on the response to natural disasters, includes the introduction of a new functional form for production functions, which allowed us to create bespoke production functions for each industry based on a survey of industry analysts. We also introduced new elements for modeling inventories, consumption and labor. The resulting model made accurate real-time forecasts for the decline of sectoral and aggregate economic activity in the United Kingdom in the second quarter of 2020. We examine some of the theoretical implications of our model and find that the choice of production functions and inventory levels plays a key role in the propagation of pandemic shocks. Our work demonstrates that an out of equilibrium model calibrated against national accounting data can serve as a useful real time policy evaluation and forecasting tool.

Original language	English
Article number	104527
Journal	Journal of Economic Dynamics and Control
Volume	144
DOIs	https://doi.org/10.1016/j.jedc.2022.104527
Publication status	Published - Nov 2022
Externally published	Yes

Bibliographical note

Funding Information:
This paper supersedes our working paper released in May 2020 (Pichler et al. 2020), which will remain unpublished. We would like to thank Eric Beinhocker, David Van Dijcke, John Muellbauer and David Vines for many useful comments and discussions. This work was supported by Baillie Gifford, Partners for a New Economy, the UK's Economic and Social Research Council (ESRC) via the Rebuilding Macroeconomics Network (Grant Ref: ES/R00787X/1), the Oxford Martin Programme on the Post-Carbon Transition, James S. McDonnell Foundation, and the Institute for New Economic Thinking at the Oxford Martin School. This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract no. 2019–1902010003. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the US Government. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. We appreciate that IHS Markit provided us with a survey on critical vs. non-critical inputs. (Note that JDF is on their advisory board). We thank Diana Beltekian for excellent research assistance.

Funding Information:
This paper supersedes our working paper released in May 2020 (Pichler et al., 2020), which will remain unpublished. We would like to thank Eric Beinhocker, David Van Dijcke, John Muellbauer and David Vines for many useful comments and discussions. This work was supported by Baillie Gifford, Partners for a New Economy, the UK’s Economic and Social Research Council (ESRC) via the Rebuilding Macroeconomics Network (Grant Ref: ES/R00787X/1), the Oxford Martin Programme on the Post-Carbon Transition, James S. McDonnell Foundation, and the Institute for New Economic Thinking at the Oxford Martin School. This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract no. 2019–1902010003. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the US Government. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. We appreciate that IHS Markit provided us with a survey on critical vs. non-critical inputs. (Note that JDF is on their advisory board). We thank Diana Beltekian for excellent research assistance.

Publisher Copyright:
© 2022 The Author(s)

Keywords

COVID-19
Inventories
Out-of-equilibrium modeling
Production function
Production network

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10.1016/j.jedc.2022.104527

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Cite this

@article{24c380ff620e408d9db8707614b6d5c6,

title = "Forecasting the propagation of pandemic shocks with a dynamic input-output model",

abstract = "We introduce a dynamic disequilibrium input-output model that was used to forecast the economics of the COVID-19 pandemic. This model was designed to understand the upstream and downstream propagation of the industry-specific demand and supply shocks caused by COVID-19, which were exceptional in their severity, suddenness and heterogeneity across industries. The model, which was inspired in part by previous work on the response to natural disasters, includes the introduction of a new functional form for production functions, which allowed us to create bespoke production functions for each industry based on a survey of industry analysts. We also introduced new elements for modeling inventories, consumption and labor. The resulting model made accurate real-time forecasts for the decline of sectoral and aggregate economic activity in the United Kingdom in the second quarter of 2020. We examine some of the theoretical implications of our model and find that the choice of production functions and inventory levels plays a key role in the propagation of pandemic shocks. Our work demonstrates that an out of equilibrium model calibrated against national accounting data can serve as a useful real time policy evaluation and forecasting tool.",

keywords = "COVID-19, Inventories, Out-of-equilibrium modeling, Production function, Production network",

author = "Anton Pichler and Marco Pangallo and {del Rio-Chanona}, {R. Maria} and Fran{\c c}ois Lafond and Farmer, {J. Doyne}",

note = "Funding Information: This paper supersedes our working paper released in May 2020 (Pichler et al. 2020), which will remain unpublished. We would like to thank Eric Beinhocker, David Van Dijcke, John Muellbauer and David Vines for many useful comments and discussions. This work was supported by Baillie Gifford, Partners for a New Economy, the UK's Economic and Social Research Council (ESRC) via the Rebuilding Macroeconomics Network (Grant Ref: ES/R00787X/1), the Oxford Martin Programme on the Post-Carbon Transition, James S. McDonnell Foundation, and the Institute for New Economic Thinking at the Oxford Martin School. This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract no. 2019–1902010003. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the US Government. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. We appreciate that IHS Markit provided us with a survey on critical vs. non-critical inputs. (Note that JDF is on their advisory board). We thank Diana Beltekian for excellent research assistance. Funding Information: This paper supersedes our working paper released in May 2020 (Pichler et al., 2020), which will remain unpublished. We would like to thank Eric Beinhocker, David Van Dijcke, John Muellbauer and David Vines for many useful comments and discussions. This work was supported by Baillie Gifford, Partners for a New Economy, the UK{\textquoteright}s Economic and Social Research Council (ESRC) via the Rebuilding Macroeconomics Network (Grant Ref: ES/R00787X/1), the Oxford Martin Programme on the Post-Carbon Transition, James S. McDonnell Foundation, and the Institute for New Economic Thinking at the Oxford Martin School. This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract no. 2019–1902010003. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the US Government. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. We appreciate that IHS Markit provided us with a survey on critical vs. non-critical inputs. (Note that JDF is on their advisory board). We thank Diana Beltekian for excellent research assistance. Publisher Copyright: {\textcopyright} 2022 The Author(s)",

year = "2022",

month = nov,

doi = "10.1016/j.jedc.2022.104527",

language = "English",

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journal = "Journal of Economic Dynamics and Control",

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T1 - Forecasting the propagation of pandemic shocks with a dynamic input-output model

AU - Pichler, Anton

AU - Pangallo, Marco

AU - del Rio-Chanona, R. Maria

AU - Lafond, François

AU - Farmer, J. Doyne

N1 - Funding Information: This paper supersedes our working paper released in May 2020 (Pichler et al. 2020), which will remain unpublished. We would like to thank Eric Beinhocker, David Van Dijcke, John Muellbauer and David Vines for many useful comments and discussions. This work was supported by Baillie Gifford, Partners for a New Economy, the UK's Economic and Social Research Council (ESRC) via the Rebuilding Macroeconomics Network (Grant Ref: ES/R00787X/1), the Oxford Martin Programme on the Post-Carbon Transition, James S. McDonnell Foundation, and the Institute for New Economic Thinking at the Oxford Martin School. This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract no. 2019–1902010003. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the US Government. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. We appreciate that IHS Markit provided us with a survey on critical vs. non-critical inputs. (Note that JDF is on their advisory board). We thank Diana Beltekian for excellent research assistance. Funding Information: This paper supersedes our working paper released in May 2020 (Pichler et al., 2020), which will remain unpublished. We would like to thank Eric Beinhocker, David Van Dijcke, John Muellbauer and David Vines for many useful comments and discussions. This work was supported by Baillie Gifford, Partners for a New Economy, the UK’s Economic and Social Research Council (ESRC) via the Rebuilding Macroeconomics Network (Grant Ref: ES/R00787X/1), the Oxford Martin Programme on the Post-Carbon Transition, James S. McDonnell Foundation, and the Institute for New Economic Thinking at the Oxford Martin School. This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract no. 2019–1902010003. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the US Government. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. We appreciate that IHS Markit provided us with a survey on critical vs. non-critical inputs. (Note that JDF is on their advisory board). We thank Diana Beltekian for excellent research assistance. Publisher Copyright: © 2022 The Author(s)

PY - 2022/11

Y1 - 2022/11

N2 - We introduce a dynamic disequilibrium input-output model that was used to forecast the economics of the COVID-19 pandemic. This model was designed to understand the upstream and downstream propagation of the industry-specific demand and supply shocks caused by COVID-19, which were exceptional in their severity, suddenness and heterogeneity across industries. The model, which was inspired in part by previous work on the response to natural disasters, includes the introduction of a new functional form for production functions, which allowed us to create bespoke production functions for each industry based on a survey of industry analysts. We also introduced new elements for modeling inventories, consumption and labor. The resulting model made accurate real-time forecasts for the decline of sectoral and aggregate economic activity in the United Kingdom in the second quarter of 2020. We examine some of the theoretical implications of our model and find that the choice of production functions and inventory levels plays a key role in the propagation of pandemic shocks. Our work demonstrates that an out of equilibrium model calibrated against national accounting data can serve as a useful real time policy evaluation and forecasting tool.

AB - We introduce a dynamic disequilibrium input-output model that was used to forecast the economics of the COVID-19 pandemic. This model was designed to understand the upstream and downstream propagation of the industry-specific demand and supply shocks caused by COVID-19, which were exceptional in their severity, suddenness and heterogeneity across industries. The model, which was inspired in part by previous work on the response to natural disasters, includes the introduction of a new functional form for production functions, which allowed us to create bespoke production functions for each industry based on a survey of industry analysts. We also introduced new elements for modeling inventories, consumption and labor. The resulting model made accurate real-time forecasts for the decline of sectoral and aggregate economic activity in the United Kingdom in the second quarter of 2020. We examine some of the theoretical implications of our model and find that the choice of production functions and inventory levels plays a key role in the propagation of pandemic shocks. Our work demonstrates that an out of equilibrium model calibrated against national accounting data can serve as a useful real time policy evaluation and forecasting tool.

KW - COVID-19

KW - Inventories

KW - Out-of-equilibrium modeling

KW - Production function

KW - Production network

U2 - 10.1016/j.jedc.2022.104527

DO - 10.1016/j.jedc.2022.104527

M3 - Journal article

AN - SCOPUS:85138760734

SN - 0165-1889

VL - 144

JO - Journal of Economic Dynamics and Control

JF - Journal of Economic Dynamics and Control

M1 - 104527

ER -