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Optimally Imprecise Memory and Biased Forecasts

Rava Azeredo Da Silveira (), Yeji Sung and Michael Woodford ()
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Rava Azeredo Da Silveira: Biophysique et Neuroscience Théoriques - LPENS (UMR_8023) - Laboratoire de physique de l'ENS - ENS Paris - ENS Paris - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - SU - Sorbonne Université - UP - Université de Paris - CNRS - Centre National de la Recherche Scientifique, Unibas - University of Basel
Yeji Sung: Columbia University [New York]

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Abstract: We propose a model of optimal decision making subject to a memory constraint. The constraint is a limit on the complexity of memory measured using Shannon's mutual information, as in models of rational inattention; but our theory differs from that of Sims (2003) in not assuming costless memory of past cognitive states. We show that the model implies that both forecasts and actions will exhibit idiosyncratic random variation; that average beliefs will also differ from rational-expectations beliefs, with a bias that fluctuates forever with a variance that does not fall to zero even in the long run; and that more recent news will be given disproportionate weight in forecasts. We solve the model under a variety of assumptions about the degree of persistence of the variable to be forecasted and the horizon over which it must be forecasted, and examine how the nature of forecast biases depends on these parameters. The model provides a simple explanation for a number of features of reported expectations in laboratory and field settings, notably the evidence of over-reaction in elicited forecasts documented by Afrouzi et al. (2020) and Bordalo et al. (2020a).

Keywords: Over-reaction; Survey expectations; Rational inattention (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-for and nep-mic
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Working Paper: Optimally Imprecise Memory and Biased Forecasts (2020) Downloads
Working Paper: Optimally Imprecise Memory and Biased Forecasts (2020) Downloads
Working Paper: Optimally Imprecise Memory and Biased Forecasts (2020) Downloads
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