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Multiattribute Shopping Models and Ridge Regression Analysis

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  • H J P Timmermans

    (Department of Architecture, Building and Planning, University of Technology, 5600 MB Eindhoven, The Netherlands)

Abstract

Policy decisions regarding retailing facilities essentially involve multiple attributes of shopping centres. If mathematical shopping models are to contribute to these decision processes, their structure should reflect the multiattribute character of retailing planning. Examination of existing models shows that most operational shopping models include only two policy variables. A serious problem in the calibration of the existing multiattribute shopping models is that of multicollinearity arising from the fact that strong linear relationships among policy variables frequently occur in real world situations. This paper points at the technique of ridge regression analysis to overcome the problem of multicollinearity in the development of multiattribute shopping models. The use of ridge regression analysis is illustrated in an application of the multiplicative competitive interaction model to spatial shopping behaviour.

Suggested Citation

  • H J P Timmermans, 1981. "Multiattribute Shopping Models and Ridge Regression Analysis," Environment and Planning A, , vol. 13(1), pages 43-56, January.
    • Handle: RePEc:sae:envira:v:13:y:1981:i:1:p:43-56
    DOI: 10.1068/a130043
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    References listed on IDEAS

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    1. Edgar Pessemier & Philip Burger & Richard Teach & Douglas Tigert, 1971. "Using Laboratory Brand Preference Scales to Predict Consumer Brand Purchases," Management Science, INFORMS, vol. 17(6), pages 371-385, February.
    2. David A. Belsley, 1976. "Multicollinearity: Diagnosing its Presence and Assessing the Potential Damage It Causes Least Squares Estimation," NBER Working Papers 0154, National Bureau of Economic Research, Inc.
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