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

  • Richard A. Ashley
  • Christopher F. Parmeter

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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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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  1. Ashley, Richard & Tsang, Kwok Ping & Verbrugge, Randal, 2014. "Frequency Dependence in a Real-Time Monetary Policy Rule," Working Paper 1430, Federal Reserve Bank of Cleveland.
  2. Iacoviello, Matteo, 2004. "Consumption, House Prices and Collateral Constraints: A Structural Econometric Analysis," 2004 Meeting Papers 207b, Society for Economic Dynamics.
  3. 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.
  4. Richard A. Ashley & Kwok Ping Tsang, 2014. "Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach," Econometrics, MDPI, Open Access Journal, vol. 2(1), pages 72-91, March.
  5. Martin Lettau & Sydney C. Ludvigson, 2014. "Shocks and Crashes," NBER Macroeconomics Annual, University of Chicago Press, vol. 28(1), pages 293 - 354.
    • 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.
  6. Gonzalo, Jesus & Granger, Clive W J, 1995. "Estimation of Common Long-Memory Components in Cointegrated Systems," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 27-35, January.
  7. Monika Piazzesi & Martin Schneider & Selale Tuzel, 2004. "Housing, Consumption and Asset Pricing," 2004 Meeting Papers 357c, Society for Economic Dynamics.
  8. 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.
  9. 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.
  10. Nikola Dvornak & Marion Kohler, 2003. "Housing Wealth, Stock Market Wealth and Consumption: A Panel Analysis for Australia," RBA Research Discussion Papers rdp2003-07, Reserve Bank of Australia.
  11. 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.
  12. 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.
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