Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Jahrbücher für Nationalökonomie und Statistik

Journal of Economics and Statistics

Editor-in-Chief: Winker, Peter

Ed. by Büttner, Thiess / Riphahn, Regina / Smolny, Werner / Wagner, Joachim


IMPACT FACTOR 2017: 0.456

CiteScore 2017: 0.47

SCImago Journal Rank (SJR) 2017: 0.231
Source Normalized Impact per Paper (SNIP) 2017: 0.606

Online
ISSN
2366-049X
See all formats and pricing
More options …
Volume 238, Issue 3-4

Issues

OLS and 2SLS in Randomized and Conditionally Randomized Experiments

Jason Ansel / Han Hong / and Jessie Li
Published Online: 2018-07-03 | DOI: https://doi.org/10.1515/jbnst-2018-0016

Abstract

We investigate estimation and inference of the (local) average treatment effect parameter when a binary instrumental variable is generated by a randomized or conditionally randomized experiment. Under i.i.d. sampling, we show that adding covariates and their interactions with the instrument will weakly improve estimation precision of the (local) average treatment effect, but the robust OLS (2SLS) standard errors will no longer be valid. We provide an analytic correction that is easy to implement and demonstrate through Monte Carlo simulations and an empirical application the interacted estimator’s efficiency gains over the unadjusted estimator and the uninteracted covariate adjusted estimator. We also generalize our results to covariate adaptive randomization where the treatment assignment is not i.i.d., thus extending the recent contributions of Bugni, F., I.A. Canay, A.M. Shaikh (2017a), Inference Under Covariate-Adaptive Randomization. Working Paper and Bugni, F., I.A. Canay, A.M. Shaikh (2017b), Inference Under Covariate-Adaptive Randomization with Multiple Treatments. Working Paper to allow for the case of non-compliance.

Keywords: big data; data science

JEL Classification: C1; C8; C9

References

  • Bugni, F., I.A. Canay, A.M. Shaikh (2017a), Inference Under Covariate-Adaptive Randomization. Tech. rep.

  • Bugni, F., I.A. Canay, A.M. Shaikh (2017b), Inference Under Covariate-Adaptive Randomization with Multiple Treatments. Tech. rep.

  • Chen, X., H. Hong, A. Tarozzi (2008), Semiparametric Efficiency in GMM Models with Auxiliary Data. Annals of Statistics 36: 808–843.Web of ScienceCrossrefGoogle Scholar

  • Freedman, D.A. (1981), Bootstrapping Regression Models. The Annals of Statistics 9: 1218–1228.CrossrefGoogle Scholar

  • Freedman, D.A. (2008), On Regression Adjustments to Experimental Data. Advances in Applied Mathematics 40: 180–193.Web of ScienceCrossrefGoogle Scholar

  • Frolich, M. (2006), Nonparametric IV Estimation of Local Average Treatment Effects with Covariates. Journal of Econometrics 139: 35–75.Web of ScienceGoogle Scholar

  • Hahn, J. (1998), On the Role of Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects. Econometrica 66: 315–332.CrossrefGoogle Scholar

  • Hong, H., D. Nekipelov (2010), Semiparametric Efficiency in Nonlinear LATE Models. Quantitative Economics 1: 279–304.CrossrefWeb of ScienceGoogle Scholar

  • Imbens, G., J. Angrist (1994), Identification and Estimation of Local Average Treatment Effects. Econometrica 62: 467–475.CrossrefGoogle Scholar

  • Imbens, G.W., D.B. Rubin (2015), Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press.Google Scholar

  • Imbens, G. W., J. M. Wooldridge (2009), Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature 47: 5–86.CrossrefWeb of ScienceGoogle Scholar

  • Lin, W. (2013), Agnostic Notes on Regression Adjustments to Experimental Data: Reexamining Freedman Critique. The Annals of Applied Statistics 7: 295–318.CrossrefGoogle Scholar

  • Newey, W. (1994), The Asymptotic Variance of Semiparametric Estimators. Econometrica 62: 1349–82.CrossrefGoogle Scholar

  • Newey, W. (1997), Convergence Rates and Asymptotic Normality for Series Estimators. Journal of Econometrics 79: 147–168.CrossrefWeb of ScienceGoogle Scholar

  • Rosenbaum, P., D. Rubin (1983), The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika 70:41.CrossrefGoogle Scholar

  • Shao, J., X. Yu, B. Zhong (2010), A Theory for Testing Hypotheses Under Covariateadaptive Randomization. Biometrika 97: 347–360.CrossrefGoogle Scholar

About the article

Received: 2017-02-22

Revised: 2018-01-08

Accepted: 2018-02-01

Published Online: 2018-07-03

Published in Print: 2018-07-26


Citation Information: Jahrbücher für Nationalökonomie und Statistik, Volume 238, Issue 3-4, Pages 243–293, ISSN (Online) 2366-049X, ISSN (Print) 0021-4027, DOI: https://doi.org/10.1515/jbnst-2018-0016.

Export Citation

© 2018 Oldenbourg Wissenschaftsverlag GmbH, Published by De Gruyter Oldenbourg, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in