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Marketing Category Forecasting: An Alternative of BVAR‐Artificial Neural Networks *
Author(s) -
Jiang James J.,
Zhong Maosen,
Klein Gary
Publication year - 2000
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.2000.tb00943.x
Subject(s) - heteroscedasticity , autoregressive conditional heteroskedasticity , artificial neural network , autocorrelation , autoregressive model , econometrics , computer science , series (stratigraphy) , normality , time series , machine learning , bayesian probability , artificial intelligence , data mining , statistics , mathematics , volatility (finance) , paleontology , biology
Analyzing scanner data in brand management activities presents unique difficulties due to the vast quantity of the data. Time series methods that are able to handle the volume effectively often are inappropriate due to the violation of many statistical assumptions in the data characteristics. We examine scanner data sets for three brand categories and examine properties associated with many time series forecasting methods. Many violations are found with respect to linearity, normality, autocorrelation, and heteroscedasticity. With this in mind we compare the forecasting ability of neural networks that require no assumptions to two of the more robust time series techniques. Neural networks provide similar forecasts to Bayesian vector autoregression (BVAR), and both outperform generalized autoregressive conditional herteroscedasticty (GARCH) models.