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Monetary Policy Analysis When Planning Horizons Are Finite

Michael Woodford

Chapter in NBER book NBER Macroeconomics Annual 2018, volume 33 (2019), Martin Eichenbaum and Jonathan A. Parker, editors (p. 1 - 50)
Conference held April 12-13, 2018
Published in June 2019 by University of Chicago Press
© 2019 by the National Bureau of Economic Research
in NBER Book Series NBER Macroeconomics Annual

It is common to analyze the effects of alternative possible monetary policy commitments under the assumption of optimization under rational (or fully model-consistent) expectations. This implicitly assumes unrealistic cognitive abilities on the part of economic decision makers. The relevant question, however, is not whether the assumption can be literally correct, but how much it would matter to model decision making in a more realistic way. A model is proposed, based on the architecture of arti ficial intelligence programs for problems such as chess or go, in which decision makers look ahead only a finite distance into the future, and use a value function learned from experience to evaluate situations that may be reached after a finite sequence of actions by themselves and others. Conditions are discussed under which the predictions of a model with finite-horizon forward planning are similar to those of a rational expectations equilibrium, and under which they are instead quite different. The model is used to re-examine the consequences that should be expected from a central-bank commitment to maintain a fixed nominal interest rate for a substantial period of time. "Neo-Fisherian" predictions are shown to depend on using rational expectations equilibrium analysis under circumstances in which it should be expected to be unreliable.

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Document Object Identifier (DOI): https://doi.org/10.1086/700892

This chapter first appeared as NBER working paper w24692, Monetary Policy Analysis when Planning Horizons are Finite, Michael Woodford
Commentary on this chapter:
  Comment, Jennifer La’O
  Comment, Guido Lorenzoni
  Discussion,
 
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