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Discrete Adjustment to a Changing Environment: Experimental Evidence

Mel Win Khaw, Luminita Stevens and Michael Woodford ()

No 11725, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 used in the literature on price adjustment.

Keywords: adjustment hazard; generalized Ss model; menu costs; rational inattention (search for similar items in EconPapers)
JEL-codes: C91 D84 E03 (search for similar items in EconPapers)
Date: 2016-12
New Economics Papers: this item is included in nep-exp
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Related works:
Journal Article: Discrete adjustment to a changing environment: Experimental evidence (2017) Downloads
Working Paper: Discrete Adjustment to a Changing Environment: Experimental Evidence (2016) Downloads
Working Paper: Discrete Adjustment to a Changing Environment: Experimental Evidence (2016) Downloads
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