Application of an Adaptive Step-Size Algorithm in Models of Hyperinflation

An adaptive step-size algorithm [Kushner and Yin, Stochastic Approximation and Recursive Algorithms and Applications, 2nd ed., New York: Springer-Verlag (2003)] is used to model time-varying learning, and its performance is illustrated in the environment of Marcet and Nicolini [American Economic Review 93 (2003), 1476–1498]. The resulting model gives qualitatively similar results to those of Marcet and Nicolini, and performs quantitatively somewhat better, based on the criterion of mean squared error. The model generates increasing gain during hyperinflations and decreasing gain after hyperinflations end, which matches findings in the data. An agent using this model behaves cautiously when faced with sudden changes in policy, and is able to recognize a regime change after acquiring sufficient information

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.

Learning Benevolent Leadership in a Heterogenous Agents Economy

This paper studies the potential commitment value of cheap talkinflation announcements in an agent-based dynamic extension of theKydland-Prescott model. In every period, the policy maker makesa non-binding inflation announcement before setting the actualinflation rate. It updates its decisions using individual evolutionarylearning. The private agents can choose between two differentforecasting strategies: They can either set their forecast equal tothe announcement or compute it, at a cost, using an adaptive learningscheme. They switch between these two strategies as a function ofinformation about the associated payoffs they obtain throughword-of-mouth, choosing always the currently most favorable one.Weshow that the policy maker is able to sustain a situation with apositive but fluctuating fraction of believers. This equilibrium isPareto superior to the outcome predicted by standard theory. Theinfluence of changes in key parameters and the impact of transmissionof information among nonbelievers on the dynamics are studied.time inconsistency; bounded rationality; forecast and agentheterogeneity; cheap talk; evolutionary learning

Are sunspots learnable? An experimental investigation in a simple macroeconomic model

We conduct experiments with human subjects in a model with a positive production externality in which productivity is a nondecreasing function of the average level of employment of other firms. The model has three steady states and a sunspot equilibrium that fluctuates between the high and low steady states. Steady states are payoff ranked: low values give lower profits than higher values. We investigate whether subjects can learn a sunspot equilibrium. We observe coordination on the extrinsic announcements in our experimental economies. Cases of apparent convergence to the low and high steady states are also observed.PostprintPeer reviewe

Learning and monetary policy

This thesis studies implications of different learning mechanisms in various monetary environments. In Chapter 2, adaptive step-size algorithm (Kushner, Yin 2003) is used to model time-varying learning and is studied in the environment of Marcet, Nicolini (2003). The resulting model gives qualitatively similar results to MN and performs quantitatively somewhat better based on the criterion of mean squared error. This model generates increasing gain during hyperinflations that matches findings in Cagan (1956), Khan (1977). An agent behaves cautiously when faced with sudden changes in policy, and is able to recognize a change in regime after acquiring sufficient information. Chapter 3 analyzes the effects of social learning in New Keynesian model described in Woodford (2003). The question is whether the economy will converge to a rational expectations equilibrium under this more realistic learning dynamics. A key result from the literature in this version of the model 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. The finding is that the Taylor Principle is not necessary for convergence in a social learning context. This paper also contributes to the use of genetic algorithm learning in stochastic environments. Chapter 4 studies cheap talk announcement in an agent-based dynamic extension of Kydland-Prescott model. The government choose inflation announcement and actual inflation and updates its decisions using a model of individual, evolutionary learning (Arifovic, Ledyard 2004). Private agents use naïve and more sophisticated inflati on forecasts and switch between them based on their payoffs. Agents and government can coordinate on Pareto-superior outcomes with positive fraction of naive agents. However, the economy does not stay there. It exhibits recurrent fluctuations in announced and actual inflation as government repeatedly builds up and exploits the proportion of believers. Outcomes with higher fraction of naive forecasters have higher average welfare of agents and government. When cost of sophisticated forecast goes up, the proportion of naive believers goes up. When nonbelievers update slower, naive believers are more likely to disappear. Therefore, quick and accurate sophisticated forecasters ensure positive number of naive agents

Are sunspots learnable? An experimental investigation in a simple macroeconomic model

We conduct experiments with human subjects in a model with a positive production externality in which productivity is a nondecreasing function of the average level of employment of other firms. The model has three steady states and a sunspot equilibrium that fluctuates between the high and low steady states. Steady states are payoff ranked: low values give lower profits than higher values. We investigate whether subjects can learn a sunspot equilibrium. We observe coordination on the extrinsic announcements in our experimental economies. Cases of apparent convergence to the low and high steady states are also observed