Federal Reserve Bank of Dallas
A New Way to Quantify the Effect of Uncertainty
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.
Cite this item
Alexander W. Richter & Nathaniel Throckmorton, A New Way to Quantify the Effect of Uncertainty, Federal Reserve Bank of Dallas, Working Papers 1705, 04 May 2017, revised 23 Feb 2018.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
Keywords: Endogenous uncertainty; stochastic volatility; particle filter; zero lower bound
This item with handle RePEc:fip:feddwp:1705
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