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The performance of tests on endogeneity of subsets of explanatory variables scanned by simulation

Author

Listed:
  • Jan F. KIVIET

    (Division of Economics, Nanyang Technological University, Singapore 637332, Singapore)

  • Milan PLEUS

    (Department of Quantitative Economics, Amsterdam School of Economics, University of Amsterdam, Valckenierstraat 65, 1018 XE Amsterdam, The Netherlands)

Abstract
Tests for classification as endogenous or predetermined of arbitrary subsets of regressors are formulated as significance tests in auxiliary IV regressions and their relationships with various more classic test procedures are examined. Simulation experiments are designed by solving the data generating process parameters from salient econometric features, namely: degree of simultaneity and multicollinearity of regressors, and individual and joint strength of external instrumental variables. Thus, for various test implementations, a wide class of relevant cases is scanned for flaws in performance regarding type I and II errors. Substantial size distortions occur, but these can be cured remarkably well through bootstrapping, except when instruments are weak. The power of the subset tests is such that they establish an essential addition to the well-known classic full-set DWH tests in a data based classification of individual explanatory variables.

Suggested Citation

  • Jan F. KIVIET & Milan PLEUS, 2012. "The performance of tests on endogeneity of subsets of explanatory variables scanned by simulation," Economic Growth Centre Working Paper Series 1208, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    • Handle: RePEc:nan:wpaper:1208
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    Cited by:

    1. Kiviet, Jan F., 2020. "Testing the impossible: Identifying exclusion restrictions," Journal of Econometrics, Elsevier, vol. 218(2), pages 294-316.
    2. Doko Tchatoka, Firmin Sabro, 2012. "Specification Tests with Weak and Invalid Instruments," MPRA Paper 40185, University Library of Munich, Germany.
    3. Firmin Doko Tchatoka & Jean‐Marie Dufour, 2014. "Identification‐robust inference for endogeneity parameters in linear structural models," Econometrics Journal, Royal Economic Society, vol. 17(1), pages 165-187, February.
    4. Firmin DOKO TCHATOKA & Jean-Marie DUFOUR, 2016. "Exogeneity Tests, Incomplete Models, Weak Identification and Non-Gaussian Distributions : Invariance and Finite-Sample Distributional Theory," Cahiers de recherche 14-2016, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    5. Firmin Doko Tchatoka, 2015. "On bootstrap validity for specification tests with weak instruments," Econometrics Journal, Royal Economic Society, vol. 18(1), pages 137-146, February.
    6. Katarzyna Iwińska & Athanasios Kampas & Kerry Longhurst, 2019. "Interactions between Democracy and Environmental Quality: Toward a More Nuanced Understanding," Sustainability, MDPI, vol. 11(6), pages 1-17, March.
    7. Firmin Doko Tchatoka & Jean-Marie Dufour, 2016. "Exogeneity tests, weak identification, incomplete models and non-Gaussian distributions: Invariance and finite-sample distributional theory," School of Economics and Public Policy Working Papers 2016-01, University of Adelaide, School of Economics and Public Policy.
    8. Doko Tchatoka, Firmin & Dufour, Jean-Marie, 2020. "Exogeneity tests, incomplete models, weak identification and non-Gaussian distributions: Invariance and finite-sample distributional theory," Journal of Econometrics, Elsevier, vol. 218(2), pages 390-418.

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    More about this item

    Keywords

    bootstrapping; classification of explanatories; DWH orthogonality tests; test implementation; test performance; simulation design;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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