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Advice on using heteroskedasticity-based identification

Author

Listed:
  • Christopher F Baum

    (Boston College
    DIW Berlin)

  • Arthur Lewbel

    (Boston College)

Abstract

Lewbel (2012, Journal of Business and Economic Statistics 30: 67–80) provides a heteroskedasticity-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 command ivreg2h by Baum and Schaffer (2012, Statistical Software Components S457555, Department of Economics, Boston College). In this article, we give advice and instructions to researchers who want to use this estimator.

Suggested Citation

  • Christopher F Baum & Arthur Lewbel, 2019. "Advice on using heteroskedasticity-based identification," Stata Journal, StataCorp LP, vol. 19(4), pages 757-767, December.
    • Handle: RePEc:tsj:stataj:v:19:y:2019:i:4:p:757-767
    DOI: 10.1177/1536867X19893614
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0575/
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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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    ivreg2h; instrumental variables; linear regression; endogeneity; identification; heteroskedasticity;
    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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