Adaptive Learning in an Expectational Difference Equation with Several Lags: Selecting among Learnable REE
研究在包含技术交易的外汇市场模型中，最小二乘适应性学习能否有效减少理性预期均衡的数量，并指出在无技术交易时可通过经济意义选出唯一均衡。
Abstract It is demonstrated that adaptive learning in the least squares sense may be incapable of satisfactorily reducing the number of attainable equilibria in a rational expectations model when focusing on the forward‐solutions to the model. The model examined, as an illustration, is a basic asset pricing model for exchange rate determination that is augmented with technical trading in the currency market in the form of moving averages since it is the most commonly used technique according to questionnaire surveys. The forward‐solutions to such a model are preferable to the backward‐solutions that are normally utilized since announcement effects is an important feature in currency trade. Because of technical trading in foreign exchange, the current exchange rate depends on j max lags of the exchange rate, meaning that the model has j max + 1 rational expectations equilibria, where several of them are adaptively learnable in the least squares sense. However, since past exchange rates should not affect the current exchange rate when technical trading is absent, it is possible to single out a unique equilibrium among the adaptively learnable equilibria that is economically meaningful. It is worth noting that the model examined can also be viewed as a model for stock price determination in which the forward‐solutions to the model are preferable to the backward‐solutions since the importance of announcement effects is a common characteristic for currency and stock markets.