Working Paper: CEPR ID: DP10147
Authors: Anna Orlik; Laura Veldkamp
Abstract: A fruitful emerging literature reveals that shocks to uncertainty can explain asset returns, business cycles and financial crises. The literature equates uncertainty shocks with changes in the variance of an innovation whose distribution is common knowledge. But how do such shocks arise? This paper argues that people do not know the true distribution of macroeconomic outcomes. Like Bayesian econometricians, they estimate a distribution. Using real-time GDP data, we measure uncertainty as the conditional standard deviation of GDP growth, which captures uncertainty about the distribution?s estimated parameters. When the forecasting model admits only normally-distributed outcomes, we find small, acyclical changes in uncertainty. But when agents can also estimate parameters that regulate skewness, uncertainty fluctuations become large and counter-cyclical. The reason is that small changes in estimated skewness whip around probabilities of unobserved tail events (black swans). The resulting forecasts resemble those of professional forecasters. Our uncertainty estimates reveal that revisions in parameter estimates, especially those that affect the risk of a black swan, explain most of the shocks to uncertainty.
Keywords: forecasting; rare events; uncertainty
JEL Codes: C1; E3; G1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| uncertainty shocks (D89) | parameter estimation (C51) |
| parameter estimation (C51) | economic outcomes (F61) |
| parameter estimation (C51) | uncertainty fluctuations (D89) |
| skewness estimates (C46) | black swan events (F31) |
| uncertainty shocks (D89) | GDP growth forecasts (F17) |
| skewness estimates (C46) | uncertainty (D89) |
| mild positive realizations followed by negative observations (D91) | skewness estimates (C46) |