ABSTRACT: This paper studies
adaptive learning with multiple models. An agent operating in a
environment is aware of potential model misspecification, and tries to detect it, in real-time, using an econometric
specification test. If the current model passes the test, it is used to construct an optimal policy. If it fails the test, a new
model is selected. As the rate of coefficient updating decreases, one model becomes dominant, and is used almost
always. Dominant models can be characterized using the tools of large deviations theory. The analysis is used to address
two questions posed by Sargent's (1999) Phillips Curve model.
A Behavioral Defense of Rational Expectations
ABSTRACT: This paper
studies decision making by agents who value optimism, but are unsure of
their environment. As in
Brunnermeir and Parker (2005), an agent's optimism is assumed to be tempered by the decision costs it imposes. As in
Hansen and Sargent (2008), an agent's uncertainty about his environment leads him to formulate `robust' decision rules. It is
shown that when combined, these two considerations can lead agents to adhere to the Rational Expectations Hypothesis.
Rather than being the outcome of the sophisticated statistical calculations of an impassive expected utility maximizer, Rational
Expectations can instead be viewed as a useful approximation in environments where agents struggle to strike a balance
between doubt and hope.