## PUBLICATIONS

(Click on the title to download the paper)## Robustness and US Monetary Policy Experimentation

#### Journal of Money, Credit, and Banking, Vol. 40, No. 8, December 2008, pp. 1599-1623

We study how a concern for robustness modifies a policy maker's incentive to experiment. A policy maker has a prior over two submodels of inflation-unemployment dynamics. One submodel implies an exploitable trade-off, the other does not. Bayes' law gives the policy maker an incentive to experiment. The policy maker fears that both submodels and prior probability distribution over them are misspecified. We compute decision rules that are robust to misspecifications of the dynamics posited by each submodel as well as the prior distribution over submodels. We compare robust rules to ones that Cogley, Colacito, and Sargent (2007) computed assuming that the models and the prior distribution are correctly specified. We explain why the policy maker's desires to protect against misspecifications of the submodels, on the one hand, and misspecifications of the prior over them, on the other, have different effects on the decision rule. (With Tim W. Cogley, Lars Peter Hansen and Tom J. Sargent)## Term structure of risk, the role of Known and Unknown Risks and Non-stationary Distributions

#### Forthcoming in The Known, the Unknown and the Unknowable in Financial Risk Management, edited by F.X. Diebold

In this paper we document the presence of a time structure of risk and we propose how to measure it using alternative models to forecast volatility and the VaR at different horizons. We then quantify the benefits of an investor that is aware of the existence of a term structure of risk in the context of an asset allocation exercise. (With Robert Engle)## Benefits from U.S. Monetary Policy Experimentation in the Days of Samuelson and Solow and Lucas

#### Journal of Money Credit and Banking, Volume 39, Iss. 2, February 2007, pp. 67-100.

A policy maker knows two models of inflation-unemployment dynamics. One implies an exploitable trade-off. The other does not. The policy maker's prior probability over the two models is part of his state vector. Bayes law converts the prior into a posterior at each date and gives the policy maker an incentive to experiment. For a model calibrated to U.S. data through the early 1960s, we isolate the component of government policy that is due to experimentation by comparing the outcomes from two Bellman equations, the first of which embodies a `experiment and learn' setup, the second of which embodies a `don't experiment, do learn' view. We interpret the second as an example of an `anticipated utility' model and study how well its outcomes approximate those from the `experiment and learn' Bellman equation. (With Tim Cogley and Tom Sargent)## Testing and valuing dynamic correlation for asset allocation