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The impact of estimation uncertainty on covariate effects in nonlinear models

Author

Listed:
  • Ivan Jeliazkov

    () (University of California, Irvine)

  • Angela Vossmeyer

    () (Claremont McKenna College)

Abstract

Covariate effects are a key consideration in model evaluation, forecasting, and policy analysis, yet their dependence on estimation uncertainty has been largely overlooked in previous work. We discuss several approaches to covariate effect evaluation in nonlinear models, examine computational and reporting issues, and illustrate the practical implications of ignoring estimation uncertainty in a simulation study and applications to educational attainment and crime. The evidence reveals that failing to consider estimation variability and relying solely on parameter point estimates may lead to nontrivial biases in covariate effects that can be exacerbated in certain settings, underscoring the pivotal role that estimation uncertainty can play in this context.

Suggested Citation

  • Ivan Jeliazkov & Angela Vossmeyer, 2018. "The impact of estimation uncertainty on covariate effects in nonlinear models," Statistical Papers, Springer, vol. 59(3), pages 1031-1042, September.
  • Handle: RePEc:spr:stpapr:v:59:y:2018:i:3:d:10.1007_s00362-016-0802-7
    DOI: 10.1007/s00362-016-0802-7
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    References listed on IDEAS

    as
    1. Jeremy Verlinda, 2006. "A comparison of two common approaches for estimating marginal effects in binary choice models," Applied Economics Letters, Taylor & Francis Journals, vol. 13(2), pages 77-80.
    2. Chib, Siddhartha & Jeliazkov, Ivan, 2006. "Inference in Semiparametric Dynamic Models for Binary Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 685-700, June.
    3. Grogger, Jeffrey, 1991. "Certainty vs. Severity of Punishment," Economic Inquiry, Western Economic Association International, vol. 29(2), pages 297-309, April.
    4. Siddhartha Chib & Ivan Jeliazkov, 2005. "Accept–reject Metropolis–Hastings sampling and marginal likelihood estimation," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 59(1), pages 30-44, February.
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    Cited by:

    1. Mohammad Arshad Rahman & Angela Vossmeyer, 2019. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data," Advances in Econometrics, in: Ivan Jeliazkov & Justin L. Tobias (ed.), Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, volume 40, pages 157-191, Emerald Publishing Ltd.
    2. Angela Vossmeyer, 2019. "Analysis of Stigma and Bank Credit Provision," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(1), pages 163-194, February.

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    More about this item

    Keywords

    Covariate effect; Discrete data; Marginal effect; Nonlinear model; Partial effect;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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