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Instrument endogeneity and identification-robust tests: some analytical results

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  • Doko Tchatoka, Firmin Sabro
  • Dufour, Jean-Marie

Abstract

When some explanatory variables in a regression are correlated with the disturbance term, instrumental variable methods are typically employed to make reliable inferences. Furthermore, to avoid difficulties associated with weak instruments, identification robust methods are often proposed. However, it is hard to assess whether an instrumental variable is valid in practice because instrument validity is based on the questionable assumption that some of them are exogenous. In this paper, we focus on structural models and analyze the effects of instrument endogeneity on two identification-robust procedures, the Anderson-Rubin (1949, AR) and the Kleibergen (2002, K) tests, with or without weak instruments. Two main setups are considered: (1) the level of “instrument” endogeneity is fixed (does not depend on the sample size), and (2) the instruments are locally exogenous, i.e. the parameter which controls instrument endogeneity approaches zero as the sample size increases. In the first setup, we show that both test procedures are in general consistent against the presence of invalid instruments (hence asymptotically invalid for the hypothesis of interest), whether the instruments are “strong” or “weak”. We also describe cases where test consistency may not hold, but the asymptotic distribution is modified in a way that would lead to size distortions in large samples. These include, in particular, cases where the 2SLS estimator remains consistent, but the AR and K tests are asymptotically invalid. In the second setup, we find (non-degenerate) asymptotic non-central chi-square distributions in all cases, and describe cases where the non-centrality parameter is zero and the asymptotic distribution remains the same as in the case of valid instruments (despite the presence of invalid instruments). Overall, our results underscore the importance of checking for the presence of possibly invalid instruments when applying “identification-robust” tests.

Suggested Citation

  • Doko Tchatoka, Firmin Sabro & Dufour, Jean-Marie, 2008. "Instrument endogeneity and identification-robust tests: some analytical results," MPRA Paper 29613, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:29613
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Therese F. Azeng & Thierry U. Yogo, 2015. "Youth Unemployment, Education and Political Instability: Evidence from Selected Developing Countries 1991-2009," HiCN Working Papers 200, Households in Conflict Network.
    2. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Uniform Inference after Pretesting for Exogeneity with Heteroskedastic Data," MPRA Paper 106408, University Library of Munich, Germany.
    3. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Size-corrected Bootstrap Test after Pretesting for Exogeneity with Heteroskedastic or Clustered Data," MPRA Paper 110899, University Library of Munich, Germany.
    4. Doko Tchatoka, Firmin & Wang, Wenjie, 2020. "Uniform Inference after Pretesting for Exogeneity," MPRA Paper 99243, University Library of Munich, Germany.
    5. Caner, Mehmet, 2014. "Near exogeneity and weak identification in generalized empirical likelihood estimators: Many moment asymptotics," Journal of Econometrics, Elsevier, vol. 182(2), pages 247-268.
    6. Therese F. Azeng & Thierry Yogo Urbain, 2013. "Working Paper 171 - Youth Unemployment and Political Instability in Selected Developing Countries," Working Paper Series 467, African Development Bank.
    7. Dufour, Jean-Marie & Khalaf, Lynda & Kichian, Maral, 2010. "On the precision of Calvo parameter estimates in structural NKPC models," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1582-1595, September.
    8. Doko Tchatoka, Firmin Sabro, 2012. "Specification Tests with Weak and Invalid Instruments," MPRA Paper 40185, University Library of Munich, Germany.
    9. Tchatoka, Firmin Doko, 2015. "Subset Hypotheses Testing And Instrument Exclusion In The Linear Iv Regression," Econometric Theory, Cambridge University Press, vol. 31(6), pages 1192-1228, December.
    10. Tae-Hwan Kim & Christophe Muller, 2017. "A Robust Test of Exogeneity Based on Quantile Regressions," AMSE Working Papers 1716, Aix-Marseille School of Economics, France.
    11. Dufour, Jean-Marie & Khalaf, Lynda & Kichian, Maral, 2013. "Identification-robust analysis of DSGE and structural macroeconomic models," Journal of Monetary Economics, Elsevier, vol. 60(3), pages 340-350.
    12. Marcellino, Massimiliano & Kapetanios, George & Khalaf, Lynda, 2015. "Factor based identification-robust inference in IV regressions," CEPR Discussion Papers 10390, C.E.P.R. Discussion Papers.
    13. Armand Totouom & Vincent De Paul Mboutchouang & Hervé Kaffo Fotio, 2018. "The Effects of Education on Labour Force Participation in Cameroon: A Gender Perspective," African Development Review, African Development Bank, vol. 30(1), pages 45-55, March.
    14. Yogo, Urbain Thierry & Mallaye, Douzounet, 2012. "Social Network and Social Protection: Evidence from Cameroon," MPRA Paper 44935, University Library of Munich, Germany.
    15. Qazi Haque & Leandro M. Magnusson, 2023. "Identification Robust Empirical Evidence on the Open Economy IS‐Curve," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 345-372, April.
    16. Ruoyao Shi & Zhipeng Liao, 2018. "An Averaging GMM Estimator Robust to Misspecification," Working Papers 201803, University of California at Riverside, Department of Economics.

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    More about this item

    Keywords

    simultaneous equations; instrumental variables; locally weak instruments; invalid instruments; locally exogenous instruments.;
    All these keywords.

    JEL classification:

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

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