We introduce the Qualitative Expectations Hypothesis (QEH) as a new approach to modeling macroeconomic and Financial outcomes. Building on John Muth's seminal insight underpinning the Rational Expectations Hypothesis (REH), QEH represents the market's forecasts to be consistent with the...

We introduce the Qualitative Expectations Hypothesis (QEH) as a new approach to modeling macroeconomic and financial outcomes. Building on John Muth's seminal insight underpinning the Rational Expectations Hypothesis (REH), QEH represents the market's forecasts to be consistent with the...

The adaptive learning approach has been fruitfully employed to model the formation of aggregate expectations at the macroeconomic level, as an alternative to rational expectations. This paper uses adaptive learning to understand, instead, the formation of expectations at the micro-level, by...

Using a long-panel dataset of Japanese firms that contains firm-level sales forecasts, we provide evidence on firm-level uncertainty and imperfect information over their life cycle. We find that firms make non-negligible and positively correlated forecast errors. However, they make more precise...

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...

Using a novel dataset that contains qualitative firm survey data on sales forecasts as well as balance-sheet data on realized sales, we document that only major forecast errors are predictable and display autocorrelation. This result is a particular violation of the Full Information Rational...

We experimentally investigate how price expectations are formed in a large asset market where subjects' only task is to forecast the future price of a risky asset. The realized prices depend on these expectations. We observe small (6 participants) and large markets (about 100 participants). In...

Based on the approach advanced by Elliott et al. (Rev. Ec. Studies. 72, 1197-1125), we found that the loss function of a sample of oil price forecasters is asymmetric in the forecast error. Our findings indicate that the loss oil price forecasters incurred when their forecasts exceeded the price...

We compare forecasts from different adaptive learning algorithms and calibrations applied to US real-time data on inflation and growth. We find that the Least Squares with constant gains adjusted to match (past) survey forecasts provides the best overall performance both in terms of forecasting...