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On the choice of regularization parameters in specification testing: a critical discussion

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  • Stefan Sperlich

Abstract

This article reviews and discusses the problem of choosing smoothing parameters and resampling schemes for specification tests in econometrics. While smoothing is used for the regularization of the non-specified parts of the null hypothesis and omnibus alternatives, the resampling serves for determining the critical value. Several of the existing selection methods are discussed, implemented, and compared. This has been done for cross-sectional data along the example of additivity testing. Doubtless, all problems considered here carry over to specification testing with dependent data. Intensive simulations illustrate that this is still an open problem that easily corrupts these tests in practice. Possible ways out of the dilemma are proposed. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Stefan Sperlich, 2014. "On the choice of regularization parameters in specification testing: a critical discussion," Empirical Economics, Springer, vol. 47(2), pages 427-450, September.
    • Handle: RePEc:spr:empeco:v:47:y:2014:i:2:p:427-450
    DOI: 10.1007/s00181-013-0752-z
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    Cited by:

    1. Bianco, Ana M. & Boente, Graciela & González-Manteiga, Wenceslao & Pérez-González, Ana, 2015. "Robust inference in partially linear models with missing responses," Statistics & Probability Letters, Elsevier, vol. 97(C), pages 88-98.
    2. Henderson, Daniel J. & Sperlich, Stefan, 2022. "A Complete Framework for Model-Free Difference-in-Differences Estimation," IZA Discussion Papers 15799, Institute of Labor Economics (IZA).
    3. Jing Dai & Stefan Sperlich & Walter Zucchini, 2016. "A Simple Method for Predicting Distributions by Means of Covariates with Examples from Poverty and Health Economics," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 152(I), pages 49-80, March.
    4. Henderson, Daniel J. & Sheehan, Alice, 2018. "Kernel-based testing with skewed and heavy-tailed data: Evidence from a nonparametric test for heteroskedasticity," Economics Letters, Elsevier, vol. 172(C), pages 8-11.
    5. Ivan Korolev, 2018. "A Consistent Heteroskedasticity Robust LM Type Specification Test for Semiparametric Models," Papers 1810.07620, arXiv.org, revised Nov 2019.
    6. Xu Guo & Wangli Xu & Lixing Zhu, 2015. "Model checking for parametric regressions with response missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(2), pages 229-259, April.
    7. Stefan Sperlich, 2022. "Comments on: hybrid semiparametric Bayesian networks," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 335-339, June.
    8. Stefan Sperlich & Jose-Ramon Uriarte, 2019. "The economics of minority language use: theory and empirical evidence for a language game model," Papers 1908.11604, arXiv.org.

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    More about this item

    Keywords

    Nonparametric specification tests; Adaptive testing ; Bandwidth choice; Bootstrap; Subsampling; C12; C14; C52;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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