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Sensitivity Analysis For Inference In 2SLS Estimation With Possibly-Flawes Instruments

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  • Richard A. Ashley
  • Christopher F. Parmeter
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    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 the 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 two-stage least squares estimation, quantifying the fragility/robustness of inference with respect to possible flaws in the exogeneity assumptions made, and also indicating which of these assumptions are most crucial. The method is illustrated with an empirical application focusing on the education-earnings relationship.

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    File URL: ftp://repec.econ.vt.edu/Papers/Ashley/2SLS_IV_sensitivity_analysis.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 e07-38.

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    Length: 19 pages
    Date of creation: 2013
    Date of revision:
    Handle: RePEc:vpi:wpaper:e07-38

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    Keywords: Robustness; invalid instruments; flawed instruments; instrumental variables; sensitivity analysis; two-stage least squares.;

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    1. Richard A. Ashley & Randall J. Verbrugge., 2006. "Mis-Specification in Phillips Curve Regressions: Quantifying Frequency Dependence in This Relationship While Allowing for Feedback," Working Papers e06-11, Virginia Polytechnic Institute and State University, Department of Economics.
    2. Nikola Dvornak & Marion Kohler, 2007. "Housing Wealth, Stock Market Wealth and Consumption: A Panel Analysis for Australia," The Economic Record, The Economic Society of Australia, vol. 83(261), pages 117-130, 06.
    3. Richard A. Ashley & Kwok Ping Tsang, 2013. "Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach," Working Papers e07-41, Virginia Polytechnic Institute and State University, Department of Economics.
    4. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2007. "Enhanced routines for instrumental variables/generalized method of moments estimation and testing," Stata Journal, StataCorp LP, vol. 7(4), pages 465-506, December.
    5. Richard A. Ashley. & Randall J. Verbrugge, 2006. "Frequency Dependence in Regression Model Coefficients: An Alternative Approach for Modeling Nonlinear Dynamic Relationships in Time Series," Working Papers e06-7, Virginia Polytechnic Institute and State University, Department of Economics.
    6. Monika Piazzesi & Martin Schneider & Selale Tuzel, 2006. "Housing, Consumption, and Asset Pricing," NBER Working Papers 12036, National Bureau of Economic Research, Inc.
    7. Engelhardt, Gary V., 1996. "House prices and home owner saving behavior," Regional Science and Urban Economics, Elsevier, vol. 26(3-4), pages 313-336, June.
    8. Matteo Iacoviello, 2004. "Consumption, House Prices and Collateral Constraints: a Structural Econometric Analysis," Boston College Working Papers in Economics 589, Boston College Department of Economics, revised 13 Sep 2004.
    9. Martin Lettau & Sydney C. Ludvigson, 2013. "Shocks and Crashes," NBER Chapters, in: NBER Macroeconomics Annual 2013, Volume 28, pages 293-354 National Bureau of Economic Research, Inc.
    10. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    11. Gonzalo, J. & Granger, C., 1992. "Estimation of Common Long-Memory Components in Cointegrated Systems," Papers 4, Boston University - Department of Economics.
    12. Richard Ashley & Kwok Ping Tsang & Randal J. Verbrugge, 2010. "Frequency Dependence in a Real-Time Monetary Policy Rule," Working Papers e07-21, Virginia Polytechnic Institute and State University, Department of Economics.
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