TIME SERIES AND TURNING POINT FORECASTS: A COMPARISON OF ASSOCIATIVE MEMORIES AND BAYESIAN ECONOMETRIC TECHNIQUES APPLIED TO LESAGE'S DATA*
作者:
James E. Moore,
Robert Kalaba,
Moon Kim,
Hyeon Park,
期刊:
Journal of Regional Science
(WILEY Available online 1994)
卷期:
Volume 34,
issue 1
页码: 1-25
ISSN:0022-4146
年代: 1994
DOI:10.1111/j.1467-9787.1994.tb00852.x
出版商: Blackwell Publishing Ltd
数据来源: WILEY
摘要:
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.
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