IDEAS home Printed from https://ideas.repec.org/p/nbr/nberte/0311.html
   My bibliography  Save this paper

Two-Sample Instrumental Variables Estimators

Author

Listed:
  • Atsushi Inoue
  • Gary Solon

Abstract

Following an influential article by Angrist and Krueger (1992) on two-sample instrumental variables (TSIV) estimation, numerous empirical researchers have applied a computationally convenient two-sample two-stage least squares (TS2SLS) variant of Angrist and Krueger's estimator. In the two-sample context, unlike the single-sample situation, the IV and 2SLS estimators are numerically distinct. Our comparison of the properties of the two estimators demonstrates that the commonly used TS2SLS estimator is more asymptotically efficient than the TSIV estimator and also is more robust to a practically relevant type of sample stratification.

Suggested Citation

  • Atsushi Inoue & Gary Solon, 2005. "Two-Sample Instrumental Variables Estimators," NBER Technical Working Papers 0311, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0311
    Note: TWP
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/t0311.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Currie, Janet & Yelowitz, Aaron, 2000. "Are public housing projects good for kids?," Journal of Public Economics, Elsevier, vol. 75(1), pages 99-124, January.
    3. Andersen, Torben G & Sorensen, Bent E, 1996. "GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 328-352, July.
    4. Borjas, George J., 2004. "Food insecurity and public assistance," Journal of Public Economics, Elsevier, vol. 88(7-8), pages 1421-1443, July.
    5. Angrist, J D & Imbens, G W & Krueger, A B, 1999. "Jackknife Instrumental Variables Estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(1), pages 57-67, Jan.-Feb..
    6. Joshua D. Angrist & Alan B. Krueger, 1993. "Split Sample Instrumental Variables," Working Papers 699, Princeton University, Department of Economics, Industrial Relations Section..
    7. Murphy, Kevin M & Topel, Robert H, 2002. "Estimation and Inference in Two-Step Econometric Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 88-97, January.
    8. Joshua D. Angrist & Alan B. Krueger, 1990. "The Effect of Age at School Entry on Educational Attainment: An Application of Instrumental Variables with Moments from Two Samples," NBER Working Papers 3571, National Bureau of Economic Research, Inc.
    9. Bjorklund, Anders & Jantti, Markus, 1997. "Intergenerational Income Mobility in Sweden Compared to the United States," American Economic Review, American Economic Association, vol. 87(5), pages 1009-1018, December.
    10. Thomas S. Dee & William N. Evans, 2003. "Teen Drinking and Educational Attainment: Evidence from Two-Sample Instrumental Variables Estimates," Journal of Labor Economics, University of Chicago Press, vol. 21(1), pages 178-209, January.
    11. Tauchen, George, 1986. "Statistical Properties of Generalized Method-of-Moments Estimators of Structural Parameters Obtained from Financial Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 397-416, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiang, Di & Zhan, Lue & Bordignon, Massimo, 2020. "A reconsideration of the sugar sweetened beverage tax in a household production model," Food Policy, Elsevier, vol. 95(C).
    2. Massimo Bordignon & Di Xiang & Lue Zhan, 2018. "Predicting the Effects of a Sugar Sweetened Beverage Tax in a Household Production Model," DISCE - Working Papers del Dipartimento di Economia e Finanza def075, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    3. Xin Xu & Robert Kaestner, 2010. "The Business Cycle and Health Behaviors," NBER Working Papers 15737, National Bureau of Economic Research, Inc.
    4. Hirukawa, Masayuki & Prokhorov, Artem, 2018. "Consistent estimation of linear regression models using matched data," Journal of Econometrics, Elsevier, vol. 203(2), pages 344-358.
    5. Xu, Xin, 2013. "The business cycle and health behaviors," Social Science & Medicine, Elsevier, vol. 77(C), pages 126-136.
    6. Clémentine Florens & Eric Jondeau & Hervé Le Bihan, 2001. "Assessing GMM Estimates of the Federal Reserve Reaction Function," Econometrics 0111003, University Library of Munich, Germany.
    7. Kirill Borusyak & Peter Hull & Xavier Jaravel, 2022. "Quasi-Experimental Shift-Share Research Designs [Sampling-based vs. Design-based Uncertainty in Regression Analysis]," Review of Economic Studies, Oxford University Press, vol. 89(1), pages 181-213.
    8. Borjas, George J., 2004. "Food insecurity and public assistance," Journal of Public Economics, Elsevier, vol. 88(7-8), pages 1421-1443, July.
    9. Julie L. Hotchkiss & Anil Rupasingha & Thor Watson, 2022. "In-migration and Dilution of Community Social Capital," International Regional Science Review, , vol. 45(1), pages 36-57, January.
    10. Andersen, Torben G & Sorensen, Bent E, 1996. "GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 328-352, July.
    11. Kaushal, N., 2007. "Do food stamps cause obesity?: Evidence from immigrant experience," Journal of Health Economics, Elsevier, vol. 26(5), pages 968-991, September.
    12. Angrist, J D & Imbens, G W & Krueger, A B, 1999. "Jackknife Instrumental Variables Estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(1), pages 57-67, Jan.-Feb..
    13. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    14. Somayeh Mardaneh, 2012. "How Do Oil Shocks A¤ect the Structural Stability of Hybrid New Keynesian Phillips Curve?," Discussion Papers in Economics 12/20, Division of Economics, School of Business, University of Leicester.
    15. Xu Sun & Xiaolu Lei & Baisen Liu, 2021. "Mobility Divergence in China? Complete Comparisons of Social Class Mobility and Income Mobility," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(2), pages 687-709, January.
    16. Julie L. Hotchkiss, 2019. "US Decennial Census return rates: the role of social capital," International Journal of Social Economics, Emerald Group Publishing, vol. 46(5), pages 648-668, May.
    17. Becker, Ralf, 1998. "Die verallgemeinerte Momentenmethode: Darstellung und Anwendung," Arbeitspapiere des Instituts für Statistik und Ökonometrie 16, Johannes Gutenberg-Universität Mainz, Institut für Statistik und Ökonometrie.
    18. Kezdi, Gabor & Hahn, Jinyong & Solon, Gary, 2002. "Jackknife minimum distance estimation," Economics Letters, Elsevier, vol. 76(1), pages 35-45, June.
    19. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    20. David Roodman, 2009. "A Note on the Theme of Too Many Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 135-158, February.

    More about this item

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberte:0311. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.