Sentiments

Authors

  • George-Marios Angeletos,

    1. Dept. of Economics, Massachusetts Institute of Technology, Cambridge, MA 02142, U.S.A. and NBER; angelet@mit.edu
    Search for more papers by this author
  • Jennifer La'O

    1. University of Chicago Booth School of Business, Chicago, IL 60637, U.S.A. and NBER
    Search for more papers by this author
    • This paper supersedes earlier working papers that circulated in 2008 and 2011 under the titles “Sentiments” and “Decentralization, Communication and the Origins of Fluctuation.” For stimulating discussions and feedback, we are grateful to colleagues and to seminar participants at Bern, Chicago, Harvard, Michigan, MIT, Northwestern, NYU, PennState, Princeton, Ohio State, the Atlanta and New York FRBs, the ECB, and numerous conferences. We owe special thanks to Markus Brunnermeier, Tarek Hassan, John Hassler, Per Krusell, and Venky Venkateswaran for conference discussions of our paper, and to Rochelle Edge, Refet Gürkaynak, and Burçin Kisacikoğlu for sharing their data with us. Last but certainly not least, we are grateful to the editor, and to four anonymous referees, for constructive criticisms and detailed suggestions that greatly improved the paper.


Abstract

This paper develops a new theory of fluctuations—one that helps accommodate the notions of “animal spirits” and “market sentiment” in unique-equilibrium, rational-expectations, macroeconomic models. To this goal, we limit the communication that is embedded in a neoclassical economy by allowing trading to be random and decentralized. We then show that the business cycle may be driven by a certain type of extrinsic shocks which we call sentiments. These shocks formalize shifts in expectations of economic activity without shifts in the underlying preferences and technologies; they are akin to sunspots, but operate in unique-equilibrium models. We further show how communication may help propagate these shocks in a way that resembles the spread of fads and rumors and that gives rise to boom-and-bust phenomena. We finally illustrate the quantitative potential of our insights within a variant of the RBC model.

Ancillary