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Empirical Models of Inattentive Adjustment

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  • Michael Woodford

    (Columbia University)

  • Luminita Stevens

    (University of Maryland)

  • Mel Win Khaw

    (Columbia University)

Abstract

We consider empirical tests for a class of models of discrete adjustment in which both the decision when to adjust and the decision where to move conditional on adjustment are assumed to be optimal, but in terms of an objective that includes costs of paying closer attention to both decisions. The particular theory of attention costs that we consider is based on the theory of "rational inattention" proposed by Sims (2003, 2011) and applied to the discrete adjustment of firms' pricing policies by Woodford (2009) and Stevens (2015). We generalize the models of inattentive adjustment used in the latter two papers by relaxing the assumption that agents learn the true state upon adjustment, and by introducing the possibility of intrinsic preference for particular actions. We contrast the implications of this extended model with other models of inattention and with models that feature relative-entropy "control costs," like that of Costain and Nakov (2014).

Suggested Citation

  • Michael Woodford & Luminita Stevens & Mel Win Khaw, 2017. "Empirical Models of Inattentive Adjustment," 2017 Meeting Papers 868, Society for Economic Dynamics.
    • Handle: RePEc:red:sed017:868
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    Cited by:

    1. Khaw, Mel Win & Stevens, Luminita & Woodford, Michael, 2017. "Discrete adjustment to a changing environment: Experimental evidence," Journal of Monetary Economics, Elsevier, vol. 91(C), pages 88-103.

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