Studies of risk preference have empirically established two regularities that are inconsistent with the canonical expected utility model: (1) risk aversion over small gambles greatly exceeds risk aversion over larger stakes and (2) insurance buyers play the lottery. This paper characterizes risk...

Nearly all life-cycle models adopt Yaari's (1965) assumption that individuals know the survival probabilities that they face. Given that an individual's exact survival probabilities are likely unknown, we explore the implications of relaxing this assumption. If there is no annuity market, then...

We consider the optimal policy problem of a benevolent planner, who is uncertain about an individual's true preferences because of inconsistencies in revealed preferences across behavioral frames. We adapt theories of expected utility maximization and ambiguity aversion to characterize the...

This paper considers the appropriate stabilization objectives for monetary policy in a microfounded model with staggered price-setting. Rotemberg and Woodford (1997) and Woodford (2002) have shown that under certain conditions, a local approximation to the expected utility of the representative...

We argue that narrow framing, whereby an agent who is offered a new gamble evaluates that gamble in isolation, separately from other risks she already faces, may be a more important feature of decision-making under risk than previously realized. To demonstrate this, we present evidence on...

The performance of a given portfolio policy can in principle be evaluated by comparing its expected utility with that of the optimal policy. Unfortunately, the optimal policy is usually not computable in which case a direct comparison is impossible. In this paper we solve this problem by using...

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-of-sample forecasts, particularly for models with many variables....