## Indeterminacy and Imperfect Informationwith Thomas A. Lubik and Christian Matthes Abstract:We study equilibrium determination in an environment where two kinds of agents have different information sets: The fully informed agents know the structure of the model and observe histories of all exogenous and endogenous variables. The less in- formed agents observe only a strict subset of the full information set. All types of agents form expectations rationally, but agents with limited information need to solve a dynamic signal extraction problem to gather information about the variables they do not observe. We show that for parameters values that imply a unique equilibrium under full information, the limited information rational expectations equilibrium can be indeterminate. In a simple application of our framework to a monetary policy problem we show that limited information on part of the central bank implies indeterminate outcomes even when the Taylor Principle holds. ## Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errorswith Todd E. Clark, Michael W. McCracken - draft: pdf
- slides: pdf
Abstract:We develop uncertainty measures for point forecasts from surveys such as the Survey of Professional Forecasters, Blue Chip, or the Federal Open Market Committee’s Summary of Economic Projections. At a given point of time, these surveys typically pro- vide forecasts for macroeconomic variables at multiple horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. Compared to existing constant-variance approaches, our stochastic-volatility model improves the accuracy of uncertainty measures for survey forecasts. ## Inflation and Professional Forecast Dynamics: An Evaluation of Stickiness, Persistence and Volatilitywith James M. Nason Abstract: This paper studies the joint dynamics of U.S. inflation and the average inflation predictions of the Survey of Professional Forecasters (SPF) on a sample running from 1968Q:4 to 2014Q:2. The joint data generating process (DGP) of these data consists of the unobserved components (UC) model of Stock and Watson (2007, “Why has US inflation become harder to forecast?,” Journal of Money, Credit and Banking 39(S1), 3–33) and the sticky information (SI) forecast updating equation of Mankiw and Reis (2002, “Sticky information versus sticky prices: A proposal to re- place the New Keynesian Phillips curve,” Quarterly Journal of Economics 117, 1295–1328). We introduce time-varying inflation gap persistence into the Stock and Watson (SW)-UC model and a time-varying frequency of forecast updating into the SI forecast updating equating. These models combine to produce a nonlinear state space model. This model is estimated using Bayesian tools grounded in the particle filter, which is an implementation of sequential Monte Carlo methods. The estimates reveal the data prefer the joint DGP of time-varying frequency of SI forecast updating and a SW-UC model with time-varying persistence. The joint DGP pro- duces estimates that indicate the inflation spike of 1974 was explained most by gap inflation, but trend inflation dominates the inflation peak of the early 1980s. We also find the stochastic volatility (SV) of trend inflation exhibits negative comovement with the time-varying frequency of SI forecast updating while the SV and time-varying persistence of gap inflation often show positive comovement. Thus, the average SPF respondent is most sensitive to the impact of permanent shocks on the conditional mean of inflation.
with Benjamin K. Johannsen |

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