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Author:Arifovic, Jasmina 

Working Paper
Learning in a model of economic growth and development
We study a model of economic growth and development with a threshold externality. The model has one steady state with a low and stagnant level of income per capita and another steady state with a high and growing level of income per capita. Both of these steady states are locally stable under the perfect foresight assumption. We introduce learning into this environment. Learning acts as an equilibrium selection criterion and provides an interesting transition dynamic between steady states. We find that for sufficiently low initial values of human capital-values that would tend to characterize preindustrial economies-the system under learning spends a long period of time (an epoch) in the neighborhood of the low income steady state before finally transitioning to a neighborhood of the high income steady state. We urge that this type of transition dynamic provides a good characterization of the economic growth and development patterns that have been observed across countries.
AUTHORS: Bullard, James B.; Arifovic, Jasmina; Duffy, John
DATE: 1995

Working Paper
Social learning and monetary policy rules
We analyze the effects of social learning in a widely-studied monetary policy context. Social learning might be viewed as more descriptive of actual learning behavior in complex market economies. Ideas about how best to forecast the economy's state vector are initially heterogeneous. Agents can copy better forecasting techniques and discard those techniques which are less successful. We seek to understand whether the economy will converge to a rational expectations equilibrium under this more realistic learning dynamic. A key result from the literature in the version of the model we study is that the Taylor Principle governs both the uniqueness and the expectational stability of the rational expectations equilibrium when all agents learn homogeneously using recursive algorithms. We find that the Taylor Principle is not necessary for convergence in a social learning context. We also contribute to the use of genetic algorithm learning in stochastic environments.
AUTHORS: Arifovic, Jasmina; Bullard, James B.; Kostyshyna, Olena
DATE: 2007




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