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Integrated Conditional Moment Tests For Parametric Conditional Distributions

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  • Bierens, Herman J.
  • Wang, Li

Abstract

In this paper we propose consistent integrated conditional moment tests for the validity of parametric conditional distribution models, based on the integrated squared difference between the empirical characteristic function of the actual data and the characteristic function implied by the model. To avoid numerical evaluation of the conditional characteristic function of the model distribution, a simulated integrated conditional moment test is proposed. As an empirical application we test the validity of a few common health economic count data models.

Suggested Citation

  • Bierens, Herman J. & Wang, Li, 2012. "Integrated Conditional Moment Tests For Parametric Conditional Distributions," Econometric Theory, Cambridge University Press, vol. 28(2), pages 328-362, April.
    • Handle: RePEc:cup:etheor:v:28:y:2012:i:02:p:328-362_00
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    Cited by:

    1. Kremling, Gitte & Dikta, Gerhard, 2026. "Bootstrap-based goodness-of-fit test for parametric families of conditional distributions," Computational Statistics & Data Analysis, Elsevier, vol. 215(C).
    2. Dante Amengual & Marine Carrasco & Enrique Sentana, 2017. "Testing Distributional Assumptions Using a Continuum of Moments," Working Papers wp2018_1709, CEMFI.
    3. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    4. Hill Jonathan B., 2013. "Stochastically weighted average conditional moment tests of functional form," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(2), pages 121-139, April.
    5. Dai, Shengtao & Song, Xiaojun, 2025. "Consistent tests for semiparametric conditional independence," Statistics & Probability Letters, Elsevier, vol. 216(C).
    6. Anne Leucht & Michael Neumann, 2013. "Degenerate $$U$$ - and $$V$$ -statistics under ergodicity: asymptotics, bootstrap and applications in statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(2), pages 349-386, April.
    7. Amengual, Dante & Carrasco, Marine & Sentana, Enrique, 2020. "Testing distributional assumptions using a continuum of moments," Journal of Econometrics, Elsevier, vol. 218(2), pages 655-689.
    8. Xiaohong Chen & Sokbae Lee & Myung Hwan Seo & Myunghyun Song, 2020. "Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic," Papers 2008.11140, arXiv.org, revised Oct 2024.
    9. Gabriele Fiorentini & Enrique Sentana, 2021. "Specification tests for non‐Gaussian maximum likelihood estimators," Quantitative Economics, Econometric Society, vol. 12(3), pages 683-742, July.
    10. Simos G. Meintanis & Joseph Ngatchou-Wandji & Šárka Hudecová, 2025. "Omnibus diagnostic procedures for vector multiplicative errors models," Statistical Papers, Springer, vol. 66(2), pages 1-44, February.
    11. Hong Chen & Maik Döring & Uwe Jensen, 2018. "Test for model selection using Cramér–von Mises distance in a fixed design regression setting," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(4), pages 505-535, October.
    12. Cui Rui & Li Yuhao, 2024. "Testing Goodness-of-Fit for Conditional Distributions: A New Perspective based on Principal Component Analysis," Papers 2403.10352, arXiv.org, revised Jun 2025.
    13. Chen, Bin & Hong, Yongmiao, 2014. "A unified approach to validating univariate and multivariate conditional distribution models in time series," Journal of Econometrics, Elsevier, vol. 178(P1), pages 22-44.

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