We analyze practical aspects of implementing adaptive learning in the context of forward
looking linear models. We focus on how to set initial conditions for three popular algorithms …

An economy exhibits structural heterogeneity when the forecasts of different agents have
different effects on the determination of aggregate variables. We study the important case of …

We study the properties of the generalized stochastic gradient (GSG) learning in forward‐
looking models. GSG algorithms are a natural and convenient way to model learning when …

There is by now a large literature characterising conditions under which learning schemes
converge to rational expectations equilibria (REEs). It has been claimed that these results …

We study the extent to which self-referential adaptive learning can explain stylized asset
pricing facts in a general equilibrium framework. In particular, we analyze the effects of …

Page 1 William Scarth EE MACROECONOMICS The Development of Modern Methods for
Policy Analysis Про Page 2 Macroeconomics Page 3 Page 4 Macroeconomics The …

This paper studies the nature, the magnitude and the length of the transition after a capital
tax cut. The transition is analysed with adaptive learning, under which agents do not need to …

In recent years, the learnability of rational expectations equilibria (REE) and determinacy of
economic structures have rightfully joined the usual performance criteria among the sought …

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 …

Endogenously optimistic beliefs about future house prices can account for the increase, time-
path, and volatility of house prices in the US housing boom of the 2000s without shocks to …