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Imperfect Information and Learning

Paper Session

Saturday, Jan. 6, 2018 10:15 AM - 12:15 PM

Pennsylvania Convention Center, 111-A
Hosted By: Econometric Society

A Neoclassical Theory of the Keynesian Multiplier

George-Marios Angeletos
,
Massachusetts Institute of Technology
Chen Lian
,
Massachusetts Institute of Technology

Abstract

This paper develops a novel theory of how a drop in consumer spending, or aggregate demand, can trigger a series of feedback loops between spending, employment, and income, ultimately leading to a sizable recession. Unlike the one embedded in the New Keynesian framework, our theory does not hinge on nominal rigidities and on the failure of monetary policy to replicate flexible prices. Instead, it is based on the idea that firms and consumers alike are unable to disentangle idiosyncratic from aggregate shocks and to reach common knowledge of the latter. This in turn could be either because of an information friction or because of bounded rationality. As a result, our theory bypasses the empirical failings of old and new Philips curves. It also allows for sizable fiscal multipliers without commensurate inflationary pressures.

Learning From Prices: Amplification and Business Fluctuations

Ryan Chahrour
,
Boston College
Gaetano Gaballo
,
European Central Bank

Abstract

We provide a new theory of expectations-driven business cycles in which consumers’ learning from prices dramatically alters the effects of aggregate shocks. Learning from prices causes changes in aggregate productivity to shift aggregate beliefs, generating positive price-quantity comovement. The feedback of beliefs into prices can be so strong that even arbitrarily small productivity shocks lead to substantial fluctuations. Augmented with a public signal, the model can generate a rich mix of supply- and demand-driven fluctuations even though productivity is the only source of aggregate randomness. Our results imply that many standard identification assumptions used to disentangle supply and demand shocks may not be valid in environments in which agents learn from prices.

An Informational Rationale for Action Over Disclosure

Alexandre N. Kohlhas
,
Stockholm University

Abstract

The past two decades have seen a considerable increase in the amount of public information provided by policy makers. This paper proposes a novel rationale against such disclosure. Unlike other providers of public information, a policy maker can condition a policy instrument, such as a tax or an interest rate, on his information. This option allows the policy maker to control the influence of his information on market outcomes without the added obfuscation associated with partial disclosure. As a result, the exclusive use of a policy instrument is preferable in simple models in which externalities render full disclosure suboptimal. I show how this rationale extends from an abstract game to a micro-founded macroeconomic model in which firms learn from market prices.

Economic Agents as Imperfect Problem Solvers

Cosmin Liviu Ilut
,
Duke University
Rosen Valchev
,
Boston College

Abstract

We develop a tractable model of limited cognitive perception of the optimal policy function. Agents allocate cognitively costly reasoning effort to generate signals about the optimal action conditional on the observed objective state. Accumulated signals update beliefs about the entire function, but mostly about the optimal action conditional on states close to the realizations where reasoning occurred. Agents reason more when observing unusual states, producing state- and history-dependent responses akin to salient thinking. The typical individual and aggregate actions exhibit non-linearity, endogenous persistence and volatility clustering. Individual behavior also displays stochastic choice, biases in systematic behavior, and cross-sectional volatility clusters.
JEL Classifications
  • E3 - Prices, Business Fluctuations, and Cycles
  • D8 - Information, Knowledge, and Uncertainty