Investor Psychology and Security Market Under- and Overreactions

A Very Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix

Countries have sought to stop the spread of coronavirus disease 2019 (COVID-19) by severely restricting travel and in-person commercial activities. Here, we analyse the supply-chain effects of a set of idealized lockdown scenarios, using the latest global trade modelling framework. We find that supply-chain losses that are related to initial COVID-19 lockdowns are largely dependent on the number of countries imposing restrictions and that losses are more sensitive to the duration of a lockdown than its strictness. However, a longer containment that can eradicate the disease imposes a smaller loss than shorter ones. Earlier, stricter and shorter lockdowns can minimize overall losses. A ‘go-slow’ approach to lifting restrictions may reduce overall damages if it avoids the need for further lockdowns. Regardless of the strategy, the complexity of global supply chains will magnify losses beyond the direct effects of COVID-19. Thus, pandemic control is a public good that requires collective efforts and support to lower-capacity countries.

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Global supply-chain effects of COVID-19 control measures.

https://www.federalreserve.gov/econresdata/ifdp/2016/files/ifdp1166.pdf

The Macroeconomic Impact of Financial and Uncertainty Shocks

Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles

Uncertainty about the future rises in recessions. But is uncertainty a source of business cycle fluctuations or an endogenous response to them, and does the type of uncertainty matter? We find that sharply higher uncertainty about real economic activity in recessions is fully an endogenous response to other shocks that cause business cycle fluctuations, while uncertainty about financial markets is a likely source of the fluctuations. Financial market uncertainty has quantitatively large negative consequences for several measures of real activity including employment, production, and a broad real activity index. Such are the main conclusions drawn from estimation of three-variable structural vector autoregressions (SVAR). To establish dynamic causal effects, we exploit information from external variables and the timing of extraordinary economic events to identify the SVAR under credible interpretations of the structural shocks.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

https://papers.ssrn.com/sol3/Delivery.cfm/nber_w21803.pdf?abstractid=2703208&mirid=1

Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?

Uncertainty about the future rises in recessions. But is uncertainty a source of business cycle fluctuations or an endogenous response to them, and does the type of uncertainty matter? Answer: we find that sharply higher uncertainty about real economic activity in recessions is fully an endogenous response to other shocks that cause business cycle fluctuations, while uncertainty about financial markets is a likely source of the fluctuations. Financial market uncertainty has quantitatively large negative consequences for several measures of real activity including employment, production, and orders. Such are the main conclusions drawn from estimation of three-variable structural vector autoregressions. To establish causal effects, we propose an iterative projection IV (IPIV) approach to construct external instruments that are valid under credible interpretations of the structural shocks.

/pdf/uncertainty-and-business-cycles-exogenous-impulse-or-5d5h78f6ji.pdf

Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?†

The outbreak of COVID-19 has significantly disrupted the economy. This paper attempts to quantify the macroeconomic impact of costly and deadly disasters in recent US history, and to translate these estimates into an analysis of the likely impact of COVID-19. A costly disaster series is constructed over the sample 1980:1-2020:04 and the dynamic impact of a disaster shock on economic activity and on uncertainty is studied using a VAR. While past natural disasters are local in nature and come and go quickly, COVID-19 is a global, multi-period event. We therefore study the dynamic responses to a sequence of large disaster shocks. Even in a fairly conservative case where COVID-19 is a 5-month shock with its magnitude calibrated by the cost of March 2020 Coronavirus relief packages, the shock is forecast to lead to a cumulative loss in industrial production of 20% and in service sector employment of nearly 39% or 55 million jobs over the next 12 months. For each month that a shock of a given magnitude is prolonged from the base case, heightened macro uncertainty persists for another month.

/pdf/covid19-and-the-macroeconomic-effects-of-costly-disasters-1jn4lru2qa.pdf

Covid19 and the Macroeconomic Effects of Costly Disasters

It is well known that the covariance structure of the data alone is not enough to identify an SVAR, and the conventional approach is to impose restrictions on the parameters of the model based on a priori theoretical considerations. This paper suggests that much can be gained by requiring the properties of the identified shocks to agree with major economic events that have been realized. We first show that even without additional restrictions, the data alone are often quite informative about the quantitatively important shocks that have occurred in the sample. We propose shrinking the set of solutions by imposing two types of inequality constraints on the shocks. The first restricts the sign and possibly magnitude of the shocks during unusual episodes in history. The second restricts the correlation between the shocks and variables external to the SVAR. The methodology provides a way to assess the validity of assumptions imposed as equality constraints. The effectiveness and limitations of this approach are exemplified with three applications.

/pdf/shock-restricted-structural-vector-autoregressions-2d3484k387.pdf

Shock Restricted Structural Vector-Autoregressions

This paper combines a data rich environment with a machine learning algorithm to provide estimates of time-varying systematic expectational errors ("belief distortions") about the macroeconomy embedded in survey responses. We find that such distortions are large on average even for professional forecasters, with all respondent-types over-weighting their own forecast relative to other information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by quantitatively large amounts. To investigate the dynamic relation of belief distortions with the macroeconomy, we construct indexes of aggregate (across surveys and respondents) expectational biases in survey forecasts. Over-optimism is associated with an increase in aggregate economic activity. Our estimates provide a benchmark to evaluate theories for which information capacity constraints, extrapolation, sentiments, ambiguity aversion, and other departures from full information rational expectations play a role in business cycles.

Sai Ma

Papers

Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?

Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?†

Covid19 and the Macroeconomic Effects of Costly Disasters

Shock Restricted Structural Vector-Autoregressions

Belief Distortions and Macroeconomic Fluctuations