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Marriott Philadelphia Downtown, Meeting Room 307
Hosted By:
Society for Computational Economics
Monetary Policy, Asset Prices and Welfare
Paper Session
Sunday, Jan. 7, 2018 8:00 AM - 10:00 AM
- Chair: Jesús Fernández-Villaverde, University of Pennsylvania
Indeterminacy and Imperfect Information
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 informed 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.A Study of Forward Guidance Properties in New Keynesian Models
Abstract
During recent economic crisis, when nominal interest rates were at their effective lower bound, central banks used forward guidance -- announcements about future policy rates -- to conduct monetary policy. Policymakers believe that after the crisis, forward guidance will remain. There are theoretical examples in which forward guidance has immediate and unrealistically large effects on output and inflation -- forward guidance puzzle; see Del Negro et al. (2015). We show that under the standard assumption of forward stable equilibria, the puzzle occurs under very special (empirically implausible and socially suboptimal) monetary policy rules. Furthermore, we demonstrate that allowing for mixtures of forward- and backward-looking solutions extends the set of possible equilibria. The model's predictions about the effectiveness of forward guidance are strongly affected by an equilibrium selection.Safe Assets
Abstract
A safe asset's real value is insulated from shocks, including declines in GDP from rare macroeconomic disasters. However, in a Lucas-tree world, the aggregate risk is given by the process for GDP and cannot be altered by the creation of safe assets. Therefore, in the equilibrium of a representative-agent version of this economy, the quantity of safe assets will be nil. With heterogeneity in coefficients of relative risk aversion, safe assets can take the form of private bond issues from low-risk-aversion to high-risk-aversion agents. The model assumes Epstein-Zin/Weil preferences with common values of the intertemporal elasticity of substitution and the rate of time preference. The model achieves stationarity by allowing for random shifts in coefficients of relative risk aversion. We derive the equilibrium values of the ratio of safe to total assets, the shares of each agent in equity ownership and wealth, and the rates of return on safe and risky assets. In a baseline case, the steady-state risk-free rate is 1.0% per year, the unlevered equity premium is 4.2%, and the quantity of safe assets ranges up to 15% of economy-wide assets (comprising the capitalized value of GDP). A disaster shock leads to an extended period in which the share of wealth held by the low-risk-averse agent and the risk-free rate are low but rising, and the ratio of safe to total assets is high but falling. In the baseline model, Ricardian Equivalence holds in that added government bonds have no effect on rates of return and the net quantity of safe assets. Surprisingly, the crowding-out coefficient for private bonds with respect to public bonds is not 0 or -1 but around -0.5, a value found in some existing empirical studies.Discussant(s)
Pablo Winant
,
Bank of England
Robert Tetlow
,
Federal Reserve Board
Matthias Paustian
,
Federal Reserve Board
Marco Del Negro
,
Federal Reserve Bank of New York
JEL Classifications
- E4 - Money and Interest Rates
- C6 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling