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Working Papers

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Indeterminacy and Imperfect Information

with Thomas A. Lubik and Christian Matthes 


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 Errors

with Todd E. Clark, Michael W. McCracken


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 Volatility


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.


A Time Series Model of Interest Rates With the Effective Lower Bound

with Benjamin K. Johannsen

Modeling interest rates over samples that include the Great Recession requires taking stock of the effective lower bound (ELB) on nominal interest rates. We propose a flexible time--series approach which includes a ``shadow rate''---a notional rate that is less than the ELB during the period in which the bound is binding---without imposing no--arbitrage assumptions. The approach allows us to estimate the behavior of trend real rates as well as expected future interest rates in recent years. 


On the Reliability of Output Gap Estimates in Real Time


Real-time estimates of the Output Gap — defined as the cyclical component of GDP — have previously been shown to be unreliable, since they are subject to large revisions when new data comes in. However, this result has so far only been derived for constant parameter models. This paper uses statistical models where the volatility of shocks to trend and cycle can vary over time. In this case, output gap estimates derived from data vintages going back to the 1970s are much closer to “final” estimates derived from all available sample data. The final estimates not only fall mostly within the credible intervals generated by the real-time data. When generated from a model with stochastic volatility, these credible sets are also tighter, at least over low-volatility periods.

Discreet Commitments and Discretion of Policymakers with Private Information


This papers presents general methods to compute optimal commitment and discretion policies, when a policymaker is better informed about the realization of some shocks than the public. In this situation, public beliefs about the hidden information emerge as additional state variables, managed by the policymaker.

Under commitment, policy is additive in two components: The optimal policy, as if the government shared the public's information set and the systematic manipulation of that information set. Even under discretion, belief management imparts history dependence.

Illustrated in a New Keynesian economy with time-varying output targets of the policymaker, belief management improves outcomes compared to symmetric information. At the margin, the policymaker tries to be intransparent about policy objectives by engineering disturbances which lower public beliefs about the persistence of output targets.

Three Essays on the Determinants of Output, Inflation and Interest Rates (2007, Study Center Gerzensee) 

Ph.D. Thesis. University of Lausanne, 18 October 2007 (Dissertation Committee)