TIME SERIES AND TURNING POINT FORECASTS: A COMPARISON OF ASSOCIATIVE MEMORIES AND BAYESIAN ECONOMETRIC TECHNIQUES APPLIED TO LESAGE'S DATA*
比较了关联记忆技术与简单自回归模型在LeSage数据上的转折点预测表现，发现两者优于多数复杂计量方法，但不及动态线性模型。
ABSTRACT. Associative memory techniques are drawn from the artificial intelligence literature, and have demonstrated considerable utility for parameter identification in dynamical systems. Previous turning point forecasts constructed by LeSage are compared to forecasts generated by associative memories and simple autoregressive models. Both the associative memories and the autoregressions perform as well or better than the more complicated econometric procedures described by LeSage, with the exception of West and Harrison's (1989) dynamic linear model specification. Extensions are suggested.