This article investigates the temporal stability of pa
rameter estimates by comparing the results obtained from
ordinary least squares, ridge, and latent root regression
techniques in two models plagued with ill-conditioned data.
Ridge regression is found to provide improved individual
coefficient stability through time and also to have slightly
greater predictive accuracy beyond the original estimation
period in both models. Temporal stability of marketing
model parameters is important if the model is to prove
useful to marketing decision makers in making inferences
about the marginal impacts of individual predictor vari
ables.
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