9617501 Evans This project addresses the important and as yet unresolved issue whether expectations have power as independent variables to explain fluctuations in aggregate economic variables such as GDP or inflation. A recurrent theme in macroeconomics has been the hypothesis that expectations do matter, and that their importance is of the same order of magnitude as monetary, productivity and fiscal shocks. But standard dynamic macroeconomic models assume away the possibility of an independent role in explaining economic fluctuations and those models which allow for this possibility make unappealing assumptions. This project constructs models in which expectations have an independent role and in which such solutions are stable under adaptive learning. A new version of the seignorage model of inflation will be developed which includes a fiscal restriction (inspired in part by the European Union conditions for Monetary Union) on the maximum size of the deficit relative to GDP. When this model is studied under adaptive learning a number of novel features emerge. For example, this project shows that it my be possible to become trapped in a high inflation equilibrium, even when a low inflation equilibrium exists. However, a policy which tightens the fiscal restriction can induce the economy to move to the low inflation equilibrium. A second line of research will analyze the stability of the rational expectations equilibrium (REE) in a general dynamic multivariate linear model. Many applied macroeconomic models can be approximated by this framework. The project derives general conditions for REE to be stable under adaptive learning and apply the results to a range of examples. A third line of research develops a version of an endogenous growth model which exhibits growth cycles, i.e., equilibrium fluctuations in growth rates generated by self fulfilling shifts in business confidence and investment. The underlying mechanisms are based on (1) complementarities between capital inputs and (2) monopolistic competition in the development of new designs. Related work includes (1) a comparison to the data of the statistical time series implications of the increasing returns growth model, (2) investigation of the implications for asset prices of modeling asset demands as guided by a genetic algorithm,, (3) the development of new technical tools for analyzing the convergence of adaptive learning in the presence of multiple equilibria (4) study of the implications for convergence of the presence of heterogeneous expectations. ??
