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Statistical Papers

, Volume 59, Issue 3, pp 1031–1042 | Cite as

The impact of estimation uncertainty on covariate effects in nonlinear models

  • Ivan Jeliazkov
  • Angela Vossmeyer
Regular Article
  • 256 Downloads

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.

Keywords

Covariate effect Discrete data Marginal effect Nonlinear model Partial effect 

JEL Classification

C10 C18 C50 

References

  1. Brownstone D (2001) Discrete choice modeling for transportation. The leading edge, travel behaviour research. Pergamon, Amsterdam, pp 97–124Google Scholar
  2. Chib S, Greenberg E (1995) Understanding the Metropolis-Hastings algorithm. Am Stat 49:327–335Google Scholar
  3. Chib S, Jeliazkov I (2005) Accept–reject Metropolis-Hastings sampling and marginal likelihood estimation. Stat Neerl 59:30–44MathSciNetCrossRefzbMATHGoogle Scholar
  4. Chib S, Jeliazkov I (2006) Inference in semiparametric dynamic models for binary longitudinal data. J Am Stat Assoc 101:685–700MathSciNetCrossRefzbMATHGoogle Scholar
  5. Greene W (2008) Econometric Analysis, 6th edn. Prentice Hall, New JerseyGoogle Scholar
  6. Grogger J (1991) Certainty vs. severity of punishment. Econ Inquiry 29:297–309CrossRefGoogle Scholar
  7. Jeliazkov I, Graves J, Kutzbach M (2008) Fitting and comparison of models for multivariate ordinal outcomes. Adv Econom Bayesian Econom 23:115–156zbMATHGoogle Scholar
  8. Tierney L (1994) Markov chains for exploring posterior distributions (with discussion). Ann Stat 22:1701–1762CrossRefzbMATHGoogle Scholar
  9. Verlinda JA (2006) A comparison of two common approaches for estimating marginal effects in binary choice models. Appl Econ Lett 13:77–80CrossRefGoogle Scholar
  10. Wooldridge J (2002) Econometric analysis of cross section and panel data. MIT Press, CambridgezbMATHGoogle Scholar
  11. Wooldridge J (2009) Introductory Econometrics: a modern approach, 4th edn. South-Western, MasonGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of California, IrvineIrvineUSA
  2. 2.Robert Day School of Economics and FinanceClaremont McKenna CollegeClaremontUSA

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