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Open accessJournal ArticleDOI: 10.1371/JOURNAL.PONE.0247823

V-, U-, L- or W-shaped economic recovery after Covid-19: Insights from an Agent Based Model.

02 Mar 2021-PLOS ONE (Public Library of Science (PLoS))-Vol. 16, Iss: 3, pp 1-22
Abstract: We discuss the impact of a Covid-19-like shock on a simple model economy, described by the previously developed Mark-0 Agent-Based Model. We consider a mixed supply and demand shock, and show that depending on the shock parameters (amplitude and duration), our model economy can display V-shaped, U-shaped or W-shaped recoveries, and even an L-shaped output curve with permanent output loss. This is due to the economy getting trapped in a self-sustained "bad" state. We then discuss two policies that attempt to moderate the impact of the shock: giving easy credit to firms, and the so-called helicopter money, i.e. injecting new money into the households savings. We find that both policies are effective if strong enough. We highlight the potential danger of terminating these policies too early, although inflation is substantially increased by lax access to credit. Finally, we consider the impact of a second lockdown. While we only discuss a limited number of scenarios, our model is flexible and versatile enough to accommodate a wide variety of situations, thus serving as a useful exploratory tool for a qualitative, scenario-based understanding of post-Covid recovery. The corresponding code is available on-line.

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Topics: Shock (economics) (55%), Monetary policy (52%), Inflation (52%)
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Journal ArticleDOI: 10.1108/FS-01-2021-0025
22 Sep 2021-Foresight
Abstract: Purpose The novel coronavirus (COVID-19) leaves Indian business teetering on the edge of survival. This paper aims to set out to assess the impact of the pandemic shocks on the small and medium business segments in India. The research also explores the strategies that potentially take the segments back to recovery and growth. Design/methodology/approach The findings draw on the perspectives of academic and business people, and the authors use linear and nonlinear regression modelling under three recovery scenarios to support our arguments. Findings Evidence suggests that the shocks to business are manifold and the severity of most of the issues will aggravate as the recovery prolongs. Practical implications The paper explains the rationale of realistic strategies and compares its effects across potent recoveries. The findings are useful for both academics and business and relates to the strategic decisions that would be taken by small and medium enterprises to expedite recovery from the crisis. Originality/value The research is unique in surveying the academics and entrepreneurs about the impact of COVID-19 on Indian business.

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2 Citations


Open accessDOI: 10.1002/ADTS.202100343
23 Nov 2021-
Abstract: The COVID-19 pandemic has infected over 250 million people worldwide and killed more than 5 million as of November 2021. Many intervention strategies are utilized (e.g., masks, social distancing, vaccinations), but officials making decisions have a limited time to act. Computer simulations can aid them by predicting future disease outcomes, but they also require significant processing power or time. It is examined whether a machine learning model can be trained on a small subset of simulation runs to inexpensively predict future disease trajectories resembling the original simulation results. Using four previously published agent-based models (ABMs) for COVID-19, a decision tree regression for each ABM is built and its predictions are compared to the corresponding ABM. Accurate machine learning meta-models are generated from ABMs without strong interventions (e.g., vaccines, lockdowns) using small amounts of simulation data: the root-mean-square error (RMSE) with 25% of the data is close to the RMSE for the full dataset (0.15 vs 0.14 in one model;0.07 vs 0.06 in another). However, meta-models for ABMs employing strong interventions require much more training data (at least 60%) to achieve a similar accuracy. In conclusion, machine learning meta-models can be used in some scenarios to assist in faster decision-making. © 2021 Wiley-VCH GmbH

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Open accessJournal ArticleDOI: 10.1016/J.EUROECOREV.2021.103907
Carlo Fezzi1, Carlo Fezzi2, Valeria Fanghella3Institutions (3)
Abstract: This paper develops a methodology for tracking in real-time the impact of shocks (such as natural disasters, financial crises or pandemics) on gross domestic product (GDP) by analyzing high-frequency electricity market data. As an illustration, we estimate the GDP loss caused by COVID-19 in twelve European countries during the first wave of the pandemic. Our results are almost indistinguishable from the official statistics during the first two quarters of 2020 (the correlation coefficient is 0.98) and are validated by several robustness tests. We provide estimates that are more chronologically disaggregated and up-to-date than standard macroeconomic indicators and, therefore, can provide timely information for policy evaluation in time of crisis. Our results show that pursuing “herd immunity” did not shelter from the harmful economic impacts of the first wave of the pandemic. They also suggest that coordinating policies internationally is fundamental for minimizing spillover effects from non-pharmaceutical interventions across countries.

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Open accessPosted Content
Abstract: This is an informal and sketchy review of six topical, somewhat unrelated subjects in quantitative finance: rough volatility models; random covariance matrix theory; copulas; crowded trades; high-frequency trading & market stability; and "radical complexity" & scenario based (macro)economics. Some open questions and research directions are briefly discussed.

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Open accessPosted ContentDOI: 10.1101/2021.08.26.21262694
28 Aug 2021-medRxiv
Abstract: The COVID-19 pandemic has infected over 200 million people worldwide and killed more than 4 million as of August 2021. Many intervention strategies have been utilized by governments around the world, including masks, social distancing, and vaccinations. However, officials making decisions regarding interventions may have a limited time to act. Computer simulations can aid them by predicting future disease outcomes, but they also have limitations due to requirements on processing power or time. This paper examines whether a machine learning model can be trained on a small subset of simulation runs to inexpensively predict future disease trajectories very close to the original simulation results. Using four previously published agent-based models for COVID-19, this paper analyzes the predictions of decision tree regression machine learning models and compares them to the results of the original simulations. The results indicate that accurate machine learning meta-models can be generated from simulation models with no strong interventions (e.g., vaccines, lockdowns) using small amounts of simulation data. However, meta-models for simulation models that include strong interventions required much more training data to achieve a similar accuracy. This indicates that machine learning meta-models could be used in some scenarios to assist in faster decision making.

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Topics: Decision tree (53%)

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30 results found


Open accessJournal ArticleDOI: 10.1162/154247603770383415
Frank Smets1, Raf Wouters2Institutions (2)
Abstract: This paper develops and estimates a dynamic stochastic general equilibrium (DSGE) model with sticky prices and wages for the euro area. The model incorporates various other features such as habit formation, costs of adjustment in capital accumulation and variable capacity utilization. It is estimated with Bayesian techniques using seven key macroeconomic variables: GDP, consumption, investment, prices, real wages, employment, and the nominal interest rate. The introduction of ten orthogonal structural shocks (including productivity, labor supply, investment, preference, cost-push, and monetary policy shocks) allows for an empirical investigation of the effects of such shocks and of their contribution to business cycle e uctuations in the euro area. Using the estimated model, we also analyze the output (real interest rate) gap, dee ned as the difference between the actual and model-based potential output (real interest rate). (JEL: E4, E5)

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Topics: Dynamic stochastic general equilibrium (62%), Real wages (58%), Potential output (57%) ... show more

2,624 Citations


Open accessJournal ArticleDOI: 10.2139/SSRN.3547729
Warwick J. McKibbin1, Roshen Fernando1Institutions (1)
Abstract: The outbreak of coronavirus named COVID-19 has disrupted the Chinese economy and is spreading globally. The evolution of the disease and its economic impact is highly uncertain which makes it difficult for policymakers to formulate an appropriate macroeconomic policy response. In order to better understand possible economic outcomes, this paper explores seven different scenarios of how COVID-19 might evolve in the coming year using a modelling technique developed by Lee and McKibbin (2003) and extended by McKibbin and Sidorenko (2006). It examines the impacts of different scenarios on macroeconomic outcomes and financial markets in a global hybrid DSGE/CGE general equilibrium model. The scenarios in this paper demonstrate that even a contained outbreak could significantly impact the global economy in the short run. These scenarios demonstrate the scale of costs that might be avoided by greater investment in public health systems in all economies but particularly in less developed economies where health care systems are less developed and popultion density is high.

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864 Citations


Open accessReportDOI: 10.3386/W26882
Abstract: We extend the canonical epidemiology model to study the interaction between economic decisions and epidemics. Our model implies that people’s decision to cut back on consumption and work reduces the severity of the epidemic, as measured by total deaths. These decisions exacerbate the size of the recession caused by the epidemic. The competitive equilibrium is not socially optimal because infected people do not fully internalize the effect of their economic decisions on the spread of the virus. In our benchmark model, the best simple containment policy increases the severity of the recession but saves roughly half a million lives in the U.S.

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Topics: Recession (51%)

598 Citations


Book ChapterDOI: 10.1007/978-1-349-24002-9_19
01 Jan 1995-
Abstract: During the late 19th and early 20th centuries, the problems of the day were of a kind that led economists to concentrate on the allocation of resources and, to a lesser extent, economic growth, and to pay little attention to short-run fluctuations of a cyclical character. Since the Great Depression of the 1930s, this emphasis has been reversed. Economists now tend to concentrate on cyclical movements, to act and talk as if any improvement, however slight, in control of the cycle justified any sacrifice, however large, in the long-run efficiency, or prospects for growth, of the economic system. Proposals for the control of the cycle thus tend to be developed almost as if there were no other objectives and as if it made no difference within what general framework cyclical fluctuations take place. A consequence of this attitude is that inadequate attention is given to the possibility of satisfying both sets of objectives simultaneously.

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Topics: Economic expansion (54%), Economic stability (52%), Fiscal union (51%) ... show more

505 Citations


Open accessReportDOI: 10.3386/W26918
Abstract: We present a theory of Keynesian supply shocks: supply shocks that trigger changes in aggregate demand larger than the shocks themselves. We argue that the economic shocks associated to the COVID-19 epidemic—shutdowns, layoffs, and firm exits—may have this feature. In one-sector economies supply shocks are never Keynesian. We show that this is a general result that extend to economies with incomplete markets and liquidity constrained consumers. In economies with multiple sectors Keynesian supply shocks are possible, under some conditions. A 50% shock that hits all sectors is not the same as a 100% shock that hits half the economy. Incomplete markets make the conditions for Keynesian supply shocks more likely to be met. Firm exit and job destruction can amplify the initial effect, aggravating the recession. We discuss the effects of various policies. Standard fiscal stimulus can be less effective than usual because the fact that some sectors are shut down mutes the Keynesian multiplier feedback. Monetary policy, as long as it is unimpeded by the zero lower bound, can have magnified effects, by preventing firm exits. Turning to optimal policy, closing down contact-intensive sectors and providing full insurance payments to affected workers can achieve the first-best allocation, despite the lower per-dollar potency of fiscal policy.

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Topics: Fiscal policy (59%), Supply shock (58%), Aggregate demand (57%) ... show more

409 Citations


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