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Partial Effects in Ordered Response Models with Factor Variables

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  • Andrew Hodge
  • Sriram Shankar

Abstract

Interpretation in nonlinear regression models that include sets of dummy variables representing categories of underlying categorical variables is not straightforward. Partial effects giving the differences between each category and the reference category are routinely computed in the empirical economics literature. Yet, partial effects yielding the differences between each category and all other categories are not calculated, despite having great interpretative value. We derive the correct formulae for calculating these partial effects for an ordered probit model. The results of an application using data on subjective well-being illustrate the usefulness of the alternative partial effects.

Suggested Citation

  • Andrew Hodge & Sriram Shankar, 2014. "Partial Effects in Ordered Response Models with Factor Variables," Econometric Reviews, Taylor & Francis Journals, vol. 33(8), pages 854-868, November.
    • Handle: RePEc:taf:emetrv:v:33:y:2014:i:8:p:854-868
    DOI: 10.1080/07474938.2013.806157
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    Cited by:

    1. David Loschiavo, 2021. "Household debt and income inequality: Evidence from Italian survey data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(1), pages 61-103, March.
    2. Vassilopoulos, Achilleas & Klonaris, Stathis, 2016. "Determinants of transition in artificially discrete Markov chains using microdata," Economics Letters, Elsevier, vol. 146(C), pages 17-20.

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