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Advice on using heteroscedasticity based identification

Author

Listed:
  • Christopher F. Baum

    (Boston College
    DIW Berlin
    CESIS, KTH Royal Institute of Technology)

  • Arthur Lewbel

    (Boston College)

Abstract

Lewbel (2012) provides a heteroscedasticity based estimator for linear regression models containing an endogenous regressor when no external instruments or other such information is available. The estimator is implemented in the Stata module ivreg2h by Baum and Schaffer (2012). This note gives some advice and instructions to researchers who want to use this estimator.

Suggested Citation

  • Christopher F. Baum & Arthur Lewbel, 2018. "Advice on using heteroscedasticity based identification," Boston College Working Papers in Economics 975, Boston College Department of Economics, revised 17 Jun 2019.
    • Handle: RePEc:boc:bocoec:975
    as

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    References listed on IDEAS

    as
    1. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2003. "Instrumental variables and GMM: Estimation and testing," Stata Journal, StataCorp LP, vol. 3(1), pages 1-31, March.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, November.
    4. Adrian R Pagan & Anthony D Hall, 1983. "Diagnostic tests as residual analysis," Published Paper Series 1983-1, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    5. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    6. Todd Prono, 2014. "The Role Of Conditional Heteroskedasticity In Identifying And Estimating Linear Triangular Systems, With Applications To Asset Pricing Models That Include A Mismeasured Factor," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 800-824, August.
    7. Christopher F Baum & Arthur Lewbel & Mark E Schaffer & Oleksander Talavera, 2012. "Instrumental variables estimation using heteroskedasticity-based instruments," United Kingdom Stata Users' Group Meetings 2012 07, Stata Users Group.
    8. Arthur Lewbel, 1997. "Constructing Instruments for Regressions with Measurement Error when no Additional Data are Available, with an Application to Patents and R&D," Econometrica, Econometric Society, vol. 65(5), pages 1201-1214, September.
    9. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    10. Erickson, Timothy & Whited, Toni M., 2002. "Two-Step Gmm Estimation Of The Errors-In-Variables Model Using High-Order Moments," Econometric Theory, Cambridge University Press, vol. 18(3), pages 776-799, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    instrumental variables; linear regression; endogeneity; identification; heteroscedasticity;
    All these keywords.

    JEL classification:

    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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