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Discrete Adjustment to a Changing Environment: Experimental Evidence

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  • Mel Win Khaw
  • Luminita Stevens
  • Michael Woodford

Abstract

We conduct a laboratory experiment to shed light on the cognitive limitations that may affect the way decision makers respond to changes in their economic environment. The subjects solve a tracking problem: they estimate the probability of a binary event, which changes stochastically. The subjects observe draws and indicate their draw-by-draw estimate. Our subjects depart from the optimal Bayesian benchmark in systematic ways, but these deviations are not simply the result of some boundedly rational, but deterministic rule. Rather, there is a random element in the subjects' response to any given history of evidence. Moreover, subjects adjust their forecast in discrete jumps rather than after each new ring draw, even though there are no explicit adjustment costs. They adjust by both large and small amounts, contrary to the predictions of a simple Ss model of optimal adjustment subject to a fixed cost. Finally, subjects prefer to report "round number" probabilities, even though that requires exerting additional effort. Each of these regularities resembles the behavior of firms setting prices for their products. We develop a model of inattentive adjustment and compare its quantitative fit with alternative models of stochastic discrete adjustment.

Suggested Citation

  • Mel Win Khaw & Luminita Stevens & Michael Woodford, 2016. "Discrete Adjustment to a Changing Environment: Experimental Evidence," NBER Working Papers 22978, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:22978 Note: EFG ME
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    References listed on IDEAS

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    1. Taylor, John B, 1980. "Aggregate Dynamics and Staggered Contracts," Journal of Political Economy, University of Chicago Press, vol. 88(1), pages 1-23, February.
    2. Fernando E. Alvarez & Francesco Lippi & Luigi Paciello, 2011. "Optimal Price Setting With Observation and Menu Costs," The Quarterly Journal of Economics, Oxford University Press, vol. 126(4), pages 1909-1960.
    3. N. Gregory Mankiw & Ricardo Reis, 2002. "Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," The Quarterly Journal of Economics, Oxford University Press, vol. 117(4), pages 1295-1328.
    4. Jacopo Magnani & Aspen Gorry & Ryan Oprea, 2016. "Time and State Dependence in an Ss Decision Experiment," American Economic Journal: Macroeconomics, American Economic Association, vol. 8(1), pages 285-310, January.
    5. Cheremukhin, Anton A. & Popova, Anna & Tutino, Antonella, 2011. "Experimental evidence on rational inattention," Working Papers 1112, Federal Reserve Bank of Dallas.
    6. James Costain & Anton Nakov, 2013. "Logit price dynamics," Working Papers 1301, Banco de España;Working Papers Homepage.
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    Cited by:

    1. Xavier Gabaix, 2017. "Behavioral Inattention," NBER Working Papers 24096, National Bureau of Economic Research, Inc.
    2. James Costain, 2017. "Costly decisions and sequential bargaining," Working Papers 1729, Banco de España;Working Papers Homepage.

    More about this item

    JEL classification:

    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E03 - Macroeconomics and Monetary Economics - - General - - - Behavioral Macroeconomics

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