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Sensitivity Analysis of Inference in GMM Estimation With Possibly-Flawed Moment Conditions

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  • Richard A. Ashley
  • Christopher F. Parmeter

Abstract

Credible inference requires attention to the possible fragility of the results (p-values for key hypothesis tests) to flaws in the model assumptions, notably including the validity of any instruments used. Past sensitivity analysis has mainly consisted of experimentation with alternative model specifications and with tests of over-identifying restrictions. We provide a feasible sensitivity analysis of GMM estimation, quantifying the fragility/robustness of inference with respect to pos- sible flaws in population moment conditions of any sort and also indicating which conditions are most crucial. Two illustrative empirical applications are given: one to possible instrument flaws and another to potential explanatory variable endogeneity.

Suggested Citation

  • Richard A. Ashley & Christopher F. Parmeter, 2013. "Sensitivity Analysis of Inference in GMM Estimation With Possibly-Flawed Moment Conditions," Working Papers e07-40, Virginia Polytechnic Institute and State University, Department of Economics.
  • Handle: RePEc:vpi:wpaper:e07-40
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    File URL: ftp://repec.econ.vt.edu/Papers/Ashley/Ashley_Parmeter_Credible_GMM.pdf
    File Function: First version, 2013
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    References listed on IDEAS

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    1. Rossi, Barbara, 2005. "Optimal Tests For Nested Model Selection With Underlying Parameter Instability," Econometric Theory, Cambridge University Press, vol. 21(05), pages 962-990, October.
    2. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    3. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    6. Ashley, Richard, 1981. "Inflation and the Distribution of Price Changes across Markets: A Causal Analysis," Economic Inquiry, Western Economic Association International, vol. 19(4), pages 650-660, October.
    7. David A. Pierce & Larry D. Haugh, 1977. "Causality in temporal systems: characterizations and a survey," Special Studies Papers 87, Board of Governors of the Federal Reserve System (U.S.).
    8. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    9. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
    10. Richard Ashley & Haichun Ye, 2012. "On the Granger causality between median inflation and price dispersion," Applied Economics, Taylor & Francis Journals, vol. 44(32), pages 4221-4238, November.
    11. Jeffrey S. Racine & Christopher F. Parmeter, 2012. "Data-Driven Model Evaluation: A Test for Revealed Performance," Department of Economics Working Papers 2012-13, McMaster University.
    12. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-1167, July.
    13. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    14. Ashley, Richard, 1998. "A new technique for postsample model selection and validation," Journal of Economic Dynamics and Control, Elsevier, vol. 22(5), pages 647-665, May.
    15. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    16. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    17. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.
    18. Breitung, Jorg & Candelon, Bertrand, 2006. "Testing for short- and long-run causality: A frequency-domain approach," Journal of Econometrics, Elsevier, vol. 132(2), pages 363-378, June.
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    Cited by:

    1. Hans Gersbach & Hans Haller & Hideo Konishi, 2015. "Household formation and markets," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 59(3), pages 461-507, August.
    2. Richard Ashley & Christopher Parmeter, 2015. "Sensitivity analysis for inference in 2SLS/GMM estimation with possibly flawed instruments," Empirical Economics, Springer, vol. 49(4), pages 1153-1171, December.

    More about this item

    Keywords

    Robustness; invalid instruments; flawed instruments; instrumental variables; sensitivity analysis; GMM; generalized method of moments.;

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