A New Way to Quantify the Effect of Uncertainty
Alexander Richter () and
No 1705, Working Papers from Federal Reserve Bank of Dallas
This paper develops a new way to quantify the effect of uncertainty and other higher-order moments. First, we estimate a nonlinear model using Bayesian methods with data on uncertainty, in addition to common macro time series. This key step allows us to decompose the exogenous and endogenous sources of uncertainty, calculate the effect of volatility following the cost of business cycles literature, and generate data-driven policy functions for any higherorder moment. Second, we use the Euler equation to analytically decompose consumption into several terms—expected consumption, the ex-ante real interest rate, and the ex-ante variance and skewness of future consumption, technology growth, and inflation—and then use the policy functions to filter the data and create a time series for the effect of each term. We apply our method to a familiar New Keynesian model with a zero lower bound constraint on the nominal interest rate and two stochastic volatility shocks, but it is adaptable to a broad class of models.
Keywords: Endogenous uncertainty; stochastic volatility; particle filter; zero lower bound (search for similar items in EconPapers)
JEL-codes: C11 D81 E32 E58 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge, nep-mac and nep-ore
Date: 2017-05-04, Revised 2018-02-23
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https://www.dallasfed.org/-/media/documents/research/papers/2017/wp1705r1.pdf Revision (application/pdf)
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Working Paper: A New Way to Quantify the Effect of Uncertainty (2018)
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