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TIME SERIES AND TURNING POINT FORECASTS: A COMPARISON OF ASSOCIATIVE MEMORIES AND BAYESIAN ECONOMETRIC TECHNIQUES APPLIED TO LESAGE'S DATA *
Author(s) -
Moore James E.,
Kalaba Robert,
Kim Moon,
Park Hyeon
Publication year - 1994
Publication title -
journal of regional science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.171
H-Index - 79
eISSN - 1467-9787
pISSN - 0022-4146
DOI - 10.1111/j.1467-9787.1994.tb00852.x
Subject(s) - associative property , autoregressive model , bayesian probability , computer science , series (stratigraphy) , content addressable memory , point (geometry) , econometric model , econometrics , identification (biology) , bayesian vector autoregression , artificial intelligence , mathematics , artificial neural network , machine learning , paleontology , botany , geometry , pure mathematics , biology
. 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.