Working Paper: NBER ID: w13414
Authors: Lars E.O. Svensson; Noah M. Williams
Abstract: We study the problem of a policymaker who seeks to set policy optimally in an economy where the true economic structure is unobserved, and he optimally learns from observations of the economy. This is a classic problem of learning and control, variants of which have been studied in the past, but seldom with forward-looking variables which are a key component of modern policy-relevant models. As in most Bayesian learning problems, the optimal policy typically includes an experimentation component reflecting the endogeneity of information. We develop algorithms to solve numerically for the Bayesian optimal policy (BOP). However, computing the BOP is only feasible in relatively small models, and thus we also consider a simpler specification we term adaptive optimal policy (AOP) which allows policymakers to update their beliefs but shortcuts the experimentation motive. In our setting, the AOP is significantly easier to compute, and in many cases provides a good approximation to the BOP. We provide some simple examples to illustrate the role of learning and experimentation in an MJLQ framework.
Keywords: Bayesian learning; Adaptive policy; Optimal policy; Model uncertainty
JEL Codes: D81; E42; E52; E58
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| BAYESIAN OPTIMAL POLICY (BOP) (C11) | improved inference about the economic structure (L16) |
| improved inference about the economic structure (L16) | policymakers adjust their actions based on updated beliefs about the current mode of the economy (E61) |
| BAYESIAN OPTIMAL POLICY (BOP) (C11) | lowers expected future losses (G22) |
| learning process (J24) | policy effectiveness (D78) |
| ADAPTIVE OPTIMAL POLICY (AOP) (C61) | good approximation to BAYESIAN OPTIMAL POLICY (BOP) (C61) |
| uncertainty (D89) | larger experimentation motives (C90) |
| larger experimentation motives (C90) | differences between BOP and AOP (B00) |