Local convergence of recursive learning to steady states and cycles in stochastic nonlinear models

Article Abstract:

Rational expectations is widely used as the equilibrium concept for dynamic economic models. This has led to increased attention given to the convergence of adaptive learning phenomena and rational expectations equilibria. To contribute to an understanding of this convergence, a study was conducted to examine learning in nonlinear models characterized by intrinsic uncertainty. Illustrative mathematical examples are presented.

Rational expectations (Economics), Nonlinear theories

---

The sequential equilibrium theory of reputation building: a further test

Article Abstract:

The qualitative predictions of a reputation building model is tested in the application of a borrower-lender game. Experiment results show that the test does not explain observed behavioral variations to parameter differences. However, borrowers show a systematic response to payoff function changes but in directions outside theory prediction. These variations cannot be linked to the reputation building theory.

---

Similar abstracts:

• Abstracts: Efficient Bayesian inference for multiple change-point and mixture innovation models. Testing for neglected nonlinearity in long-memory models
• Abstracts: Unemployment variation over the business cycles: a comparison of forecasting models
• Abstracts: Preferences over solutions to the bargaining problem. Bayesian vector autoregressions with stochastic volatility
• Abstracts: Interactions of real GNP business cycles in a three-country time-series model. The use of canonical correlation analysis to identify the order of multivariate ARMA models: Simulation and application
• Abstracts: Quarterly GPPA Earnings Data: Time-series Properties and Predictive Ability Results in the Airlines Industry. Multiple time-series modelling: another look at the Canadian money and income data

This website is not affiliated with document authors or copyright owners. This page is provided for informational purposes only. Unintentional errors are possible.