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Incomplete Information and Informative Pricing: Theory and Application

Author

Listed:
  • University of California

    (San Diego)

  • Giacomo Rondina

Abstract

This paper studies the information contained in the equilibrium aggregate price level of an economy where firms make output price decisions faced with incomplete information about economy-wide disturbances. It is shown that when heterogeneously informed firms are allowed to extract information from the equilibrium aggregate price, the ability of the aggregate price to be a sufficient statistics of the underlying aggregate disturbance depends on the degree of strategic complementarity in firms' pricing strategy. As the incentive to price similarly increases, aggregate price goes from a perfect to an imperfect signal of changes in the aggregate state of the economy. More generally, this paper contributes to the expanding literature on monetary business cycle and incomplete information initiated by Woodford (2003a) in three directions. First, it proposes a set of techniques in the frequency domain that allow for the explicit derivation of individual heterogeneous expectations in a log-linear framework while preserving the tractability of the equilibrium fixed point condition. Second, it develops and solves a stylized model where aggregate price plays a key informational role for imperfectly informed firms of the type advocated by Hayek. Finally, it presents an application to monetary policy in a setting with a simple feedback rule for the supply of money.

Suggested Citation

  • University of California & Giacomo Rondina, 2008. "Incomplete Information and Informative Pricing: Theory and Application," 2008 Meeting Papers 981, Society for Economic Dynamics.
  • Handle: RePEc:red:sed008:981
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    References listed on IDEAS

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    Cited by:

    1. Yuriy Gorodnichenko, 2008. "Endogenous information, menu costs and inflation persistence," NBER Working Papers 14184, National Bureau of Economic Research, Inc.
    2. Leonardo Melosi, 2017. "Signalling Effects of Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(2), pages 853-884.
    3. Bin Chen & Jinho Choi & Juan Carlos Escanciano, 2017. "Testing for fundamental vector moving average representations," Quantitative Economics, Econometric Society, vol. 8(1), pages 149-180, March.
    4. Leonardo Melosi, 2011. "Public's Inflation Expectations and Monetary Policy," 2011 Meeting Papers 1151, Society for Economic Dynamics.
    5. Mankiw, N. Gregory & Reis, Ricardo, 2010. "Imperfect Information and Aggregate Supply," Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 5, pages 183-229, Elsevier.
    6. Leonardo Melosi, 2009. "A Likelihood Analysis of Models with Information Frictions," PIER Working Paper Archive 09-009, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    7. Huo, Zhen & Pedroni, Marcelo, 2023. "Dynamic information aggregation: Learning from the past," Journal of Monetary Economics, Elsevier, vol. 136(C), pages 107-124.
    8. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2007. "A Review of Nonfundamentalness and Identification in Structural VAR Models," LEM Papers Series 2007/22, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    9. Jennifer La'O, 2010. "Collateral Constraints and Noisy Fluctuations," 2010 Meeting Papers 780, Society for Economic Dynamics.

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