Optimally Imprecise Memory and Biased Forecasts

85 Pages Posted: 24 Nov 2020

See all articles by Rava Azeredo da Silveira

Rava Azeredo da Silveira

University of Basel

Yeji Sung

Columbia University

Michael Woodford

Columbia University, Graduate School of Arts and Sciences, Department of Economics

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Date Written: 2020

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).

JEL Classification: D840, E030, G410

Suggested Citation

Azeredo da Silveira, Rava and Sung, Yeji and Woodford, Michael, Optimally Imprecise Memory and Biased Forecasts (2020). CESifo Working Paper No. 8709, Available at SSRN: https://ssrn.com/abstract=3736491 or http://dx.doi.org/10.2139/ssrn.3736491

Rava Azeredo da Silveira

University of Basel ( email )

Petersplatz 1
Basel, CH-4003
Switzerland

Yeji Sung (Contact Author)

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

Michael Woodford

Columbia University, Graduate School of Arts and Sciences, Department of Economics ( email )

420 W. 118th Street
New York, NY 10027
United States

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