Policy risk and the business cycle

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe argument that policy risk, i.e. uncertainty about monetary and fiscal policy, has been holding back the economic recovery in the U.S. during the Great Recession has a large popular appeal. We analyze the role of policy risk in explaining business cycle fluctuations by using an estimated New Keynesian model featuring policy risk as well as uncertainty about technology. We directly measure uncertainty from aggregate time series using Sequential Monte Carlo Methods. While we find considerable evidence of policy risk in the data, we show that the "pure uncertainty"-effect of policy risk is unlikely to play a major role in business cycle fluctuations. With the estimated model, output effects are relatively small due to i) dampening general equilibrium effects that imply a low amplification and ii) counteracting partial effects of uncertainty. Finally, we show that policy risk has effects that are an order of magnitude larger than the ones of uncertainty about aggregate TFP. JEL-Classification: E32, E63, C11

What Drives Business Cycle Fluctuations: Aggregate or Idiosyncratic Uncertainty Shocks?

“…Both uncertainty time series 15 show strong evidence of serial correlation, which is more persistent for idiosyncratic uncertainty. There is also strong evidence of comovement between idiosyncratic and aggregate uncertainty, with the contemporaneous correlation being the strongest, and each series exhibiting positive and statistically significant correlations with the lagged value of the other up to 6 months.…”

“…Thus the preliminary evidence on lead-lag relationships points to both uncertainty series leading production, rather than the converse. 15 We report all correlations using the de-trended uncertainty measures only. 16 i.e.…”

“…where X is an M-variate stochastic process, µ is a vector of time-invariant means, and u is a Small firms Large firms ′ = Σ and ΣG ′ = I (15) where = −1 , which allows us to re-write (14) as:…”

Learning about fiscal policy and the effects of policy uncertainty

“…Thus agents in those environments know the policy rule coefficients in place in the current period. Another strand of the literature that studies uncertainty 3 (or risk) about future fiscal policy is represented by Born andPfeifer (2013) andFernandez-Villaverde, GuerronQuintana, Kuester, andRubio-Ramirez (2011), who study stochastic volatility in the innovations of otherwise standard fiscal policy rules. The view of uncertainty encoded in the latter two papers is quite different from both our approach as well as the approach used by Davig et al (2010) and similar papers: In our model, agents are uncertain as to how the government systematically sets its fiscal policy instruments (both currently and in the future), whereas in Born and Pfeifer (2013) and Fernandez-Villaverde et al (2011) agents are uncertain as to how important (i.e.…”

“…volatile) the random component of fiscal policy will be in the future. Davig et al (2010), Born and Pfeifer (2013) and FernandezVillaverde et al (2011) use full-information rational expectations models, whereas our approach encodes a form of bounded rationality common in the learning literature (the anticipated utility approach of Kreps (1998)), which sets the approaches further apart. The anticipated utility approach we use abstracts from precautionary behavior driven by model uncertainty on behalf of the private agents.…”