Yeji Sung

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 the over-reaction to news observed in the laboratory
by Afrouzi et al. (2020).

This paper evaluates the impact of the second series of Targeted Longer-Term Refinancing Operations (TLTRO2) on the amount of credit granted to non-financial private corporations and on the interest rates applied to loans in Italy, using data on credit transactions, bank and firm characteristics and a difference-in-differences approach. We find that TLTRO2 had a positive impact on the Italian credit market, encouraging medium-term lending to firms and reducing credit interest rates. While firms overall benefited from TLTRO2 irrespective of their risk category and size, we document heterogeneous treatment effects. Regarding firms’ risk category, the effects on credit quantities are larger for low-risk firms while those on credit interest rate are larger for high-risk firms. Regarding firms’ size, smaller firms benefited the most both in terms of amounts borrowed and interest rates. Furthermore, our evidence suggests that monetary policy transmission of TLTRO2 is stronger for banks with a low bad debt ratio in their balance sheets.

Conventional models of information frictions assume it is costly to process external information. However, these models cannot explain puzzling patterns observed in survey forecasts. I propose a model in which internal information—knowledge stored in memory—is also costly to process. The model is consistent with survey-forecast patterns and offers an estimation strategy to identify the extent of information frictions. I then explore the macroeconomic implications of these frictions. The proposed model suggests that inflation expectations are not well anchored, making it more challenging to stabilize inflation than under conventional information-friction models.