Heterogeneous learning

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 depend on the assumption of homogeneous agents and homogeneous learning. This paper analyses the stability of REEs under heterogeneous adaptive learning, for the class of self-referential linear stochastic models. Agents may differ in their initial perceptions about the evolution of the economy, the degrees of inertia in revising their expectations, or the learning rules they use. General conditions are provided for local stability of an REE. In general, it is not possible to show that stability under homogeneous learning implies stability under heterogeneous learning. To illustrate how to apply the results, several examples are provided.     

Supply-side reforms and learning dynamics

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 adjust instantaneously to the change, as with rational expectations (RE). Impulse response analysis reveals that the transition with learning is asymmetrically sensitive to the nature of the exogenous technological shock at the time of the reform. If the reform coincides with a negative shock, the transition to the new steady state is slow, whereas, if it coincides with a positive shock, it is approximately the same as the one predicted by RE. The results imply that cutting capital income taxes before or during a recession may not be an effective means for short-run fiscal stimulus.     

Asset pricing with adaptive learning

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 recursive least squares and constant gain algorithms in a production economy and a Lucas type endowment economy. We find that (a) recursive least squares learning has almost no effects on asset price behavior, since the algorithm converges relatively fast to rational expectations, (b) constant gain learning may contribute towards explaining the stock price and return volatility as well as the predictability of excess returns in the endowment economy but (c) in the production economy the effects of constant gain learning are mitigated by the persistence induced by capital accumulation. We conclude that in the context of these two commonly used models, standard linear self-referential learning does not resolve the asset pricing puzzles observed in the data.