We thank Yasin Şimşek for excellent research assistance. We acknowledge the support from ICC Research Foundation. The study is funded by ICC. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

In this paper, we examine the cyclical dynamics of a Real Business Cycle model with ambiguity averse consumers and investment irreversibility using the smooth ambiguity model of Klibanoff et al. (2005Klibanoff et al. ( , 2009. Ambiguity of belief about the productivity process arises as agents do not know the process driving variation in aggregate TFP, and they must make inferences regarding the true process at the same time as they infer the behavior of the unobserved temporary component using a Kalman filtering algorithm. Our findings may be summarized as follows. First, the standard business cycle facts hold in our framework, which are not altered significantly by changes in the degree of ambiguity aversion. Second, we demonstrate a role for information and learning effects, and show that lower initial ambiguity or greater confidence coupled with learning dynamics lowers the volatility and increases the persistence in all of the key macroeconomic variables. Third, comparing the performance of our model to the New Keynesian business cycle model of Ilut and Schneider (2014) with maxmin expected utility, we find that the version of their model without nominal and real frictions turns out to have limited success at matching the moments for the quantity variables. In the maxmin expected utility framework, the worst case scenario instills too much caution on the part of agents who, in the absence of a key set of nominal and real frictions, end up excessively reducing their responses to TFP shocks.

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 AbstractChanging time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended Phillips Curve (PC) models. It is shown that mechanical removal or modeling of simple low frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic PC models are extended to include structural time series models that describe typical time varying patterns in levels and volatilities. Forward and backward looking expectation components for inflation are incorporated and their relative importance is evaluated. Survey data on expected inflation are introduced to strengthen the information in the likelihood. Use is made of simulation based Bayesian techniques for the empirical analysis. No credible evidence is found on endogeneity and long run stability between inflation and marginal costs. Backward-looking inflation appears stronger than forward-looking one. Levels and volatilities of inflation are estimated more precisely using rich PC models. The extended PC structures compare favorably with existing basic Bayesian vector autoregressive and stochastic volatility models in terms of fit and prediction. Tails of the complete predictive distributions indicate an increase in the probability of deflation in recent years.

No abstract