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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.

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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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Bibliographic Info

Paper provided by Virginia Polytechnic Institute and State University, Department of Economics in its series Working Papers with number e07-40.

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Length: 34 pages
Date of creation: 2013
Date of revision:
Handle: RePEc:vpi:wpaper:e07-40

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Related research

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

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  7. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, Econometric Society, vol. 37(3), pages 424-38, July.
  8. 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, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
  9. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780195060119, October.
  10. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, Econometric Society, vol. 48(5), pages 1149-67, July.
  11. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers, National Bureau of Economic Research, Inc 0169, National Bureau of Economic Research, Inc.
  12. David A. Pierce & Larry D. Haugh, 1977. "Causality in temporal systems: characterizations and a survey," Special Studies Papers, Board of Governors of the Federal Reserve System (U.S.) 87, Board of Governors of the Federal Reserve System (U.S.).
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  14. Jeffrey S. Racine & Christopher F. Parmeter, 2012. "Data-Driven Model Evaluation: A Test for Revealed Performance," Department of Economics Working Papers, McMaster University 2012-13, McMaster University.
  15. Breitung, Jorg & Candelon, Bertrand, 2006. "Testing for short- and long-run causality: A frequency-domain approach," Journal of Econometrics, Elsevier, Elsevier, vol. 132(2), pages 363-378, June.
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