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...

Economic theory predicts that intertemporal decisions depend critically on expectations about future outcomes. Using the universe of professional survey forecasts for the United States, we document the behavior of the entire term structure of expectations for output growth, inflation, and the...

Using a unique data set of individual professional forecasts, we document disagreement about the future path of monetary policy, particularly at longer horizons. The stark differences in short rate forecasts imply strong disagreement about the risk-return trade-off of longer-term bonds....

Adaptive learning introduces persistence in the evolution of agents' beliefs over time. For applied purposes this is a convenient feature to help explain why economies present sluggish adjustments towards equilibrium. The pace of learning is directly determined by the gain parameter, which...

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...

We study the term structure of disagreement of professional forecasters for key macroeconomic variables. We document a novel set of facts: 1) forecasters disagree at all horizons, including the very long run; 2) the shape of the term structure of disagreement differs markedly across variables:...

Agents have foresight when they receive information about a random process above and beyond the information contained in its current and past history. In this paper, we propose an information-theoretic measure of the quantity of foresight in an information structure, and show how to separate...

This paper quantifies the amount of noise and bias in analysts' forecast of corporate earnings at various horizons. We first show analyst forecasts outperform statistical forecasts at short-horizons, but underperform at longer horizons. We next decompose the relative accuracy of these forecasts...