Thinking on Their Feet: Along Main Street

Abstract

This paper considers the problem of learning and decision-making in a dynamic stochastic economic environment by agents subject to information processing constraints. An agent endogenously chooses to operate in terms of a simplified model of the economy, which implies: a delayed, if at all, updating of the estimates of evolving states/random variables’ conditioning parameters; as well as the entropy reduction, or even its complete “folding” that drops the less important variables from the agent’s approximating model. Specifically, parameter learning is implemented relying on computational complexity theory, which produces a constrained version of the standard Kalman filter. The latter leads to a less than one-for-one reaction to the newly observed information, without the need to postulate e.g. habit formation; which is responsible for an underreaction to permanent parameter changes (“stickiness”), as well as for an overreaction to transitory shocks (“overshooting”). In a standard stochastic growth model with government transfers, such agents may fail to realize that a fiscal expansion now necessitates a compensatory fiscal contraction later, which implies the effectiveness, in certain sense, of the fiscal stimulus policy (albeit at the expense of efficiency losses) and a violation of the Ricardian equivalence. Numerical simulations suggest high fiscal multipliers, with the effects relatively stronger at times of economic recession. Being the outcomes of endogenous choices of rational agents, these results are immune to the Lucas critique.