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Model invariance when estimating random parameters with categorical variables

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  • Burton, Michael

Abstract

This paper shows that econometric models that include categorical variables are not invariant to choice of ‘base’ category when random parameters are estimated, unless they are allowed to be correlated. We show that the invariance can lead to significant increases in Type I errors, and distortions in the implied behaviour of respondents. We hypothesise that these biases may influence the economic policy implications of published models that contain this error, but it’s impossible to be sure without re-estimating the model correctly.

Suggested Citation

  • Burton, Michael, 2018. "Model invariance when estimating random parameters with categorical variables," Working Papers 273051, University of Western Australia, School of Agricultural and Resource Economics.
    • Handle: RePEc:ags:uwauwp:273051
    DOI: 10.22004/ag.econ.273051
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    Cited by:

    1. Chèze, Benoît & David, Maia & Martinet, Vincent, 2020. "Understanding farmers' reluctance to reduce pesticide use: A choice experiment," Ecological Economics, Elsevier, vol. 167(C).
    2. Wuyang Hu & Shan Sun & Jerrod Penn & Ping Qing, 2022. "Dummy and effects coding variables in discrete choice analysis," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(5), pages 1770-1788, October.
    3. Anthony PARIS & Pascal GASTINEAU & Pierre-Alexandre MAHIEU & Benoît CHEZE, 2020. "Citizen involvement in the energy transition: Highlighting the role played by the spatial heterogeneity of preferences in the public acceptance of biofuels," LEO Working Papers / DR LEO 2828, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.

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    Research Methods/ Statistical Methods;

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