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Simultaneous equation penalized likelihood estimation of vehicle accident injury severity

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  • Francesco Donat
  • Giampiero Marra

Abstract

A bivariate system of equations is developed to model ordinal polychotomous dependent variables within a simultaneous additive regression framework. The functional form of the covariate effects is assumed fairly flexible with appropriate smoothers used to account for non‐linearities and spatial variability in the data. Non‐Gaussian error dependence structures are dealt with by means of copulas whose association parameter is also specified in terms of a generic additive predictor. The framework is employed to study the effects of several risk factors on the levels of injury sustained by individuals in two‐vehicle accidents in France. The use of the methodology proposed is motivated by the presence of common unobservables that may affect the interrelationships between the parties involved in the same crash and by the possible heterogeneity in individuals’ characteristics and accident dynamics. Better calibrated estimates are obtained and misspecification reduced via an enhanced model specification.

Suggested Citation

  • Francesco Donat & Giampiero Marra, 2018. "Simultaneous equation penalized likelihood estimation of vehicle accident injury severity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(4), pages 979-1001, August.
    • Handle: RePEc:bla:jorssc:v:67:y:2018:i:4:p:979-1001
    DOI: 10.1111/rssc.12267
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    Cited by:

    1. Maike Hohberg & Francesco Donat & Giampiero Marra & Thomas Kneib, 2021. "Beyond unidimensional poverty analysis using distributional copula models for mixed ordered‐continuous outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1365-1390, November.

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