We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG...

Macroeconomic expectations of various economic agents are characterized by substantial cross-sectional heterogeneity. In this paper, we focus on expectations heterogeneity among professional forecasters. We first present stylized facts and discuss theoretical explanations for heterogeneous...

Macroeconomic expectations of various economic agents are characterized by substantial crosssectional heterogeneity. This chapter focuses on heterogeneity in the expectations among professional forecasters, first presenting stylized facts and discussing theoretical explanations for heterogeneous...

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...

This experiment compares the price dynamics and bubble formation in an asset market with a price adjustment rule in three treatments where subjects (1) submit a price forecast only, (2) choose quantity to buy/sell and (3) perform both tasks. We find deviation of the market price from the...

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...

Using a novel and unique panel dataset of individual-level professional forecasts at short, medium, and very-long horizons, we provide new stylized facts about survey forecasts. We present direct evidence that forecasters use multivariate models in an environment with imperfect information about...

Out-of-sample forecasting tests of DSGE models against time-series benchmarks such as an unrestricted VAR are increasingly used to check a) the specification b) the forecasting capacity of these models. We carry out a Monte Carlo experiment on a widely-used DSGE model to investigate the power of...