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Instrumental Variables: An Econometrician's Perspective

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  • Imbens, Guido W.

    () (Stanford University)

Abstract

I review recent work in the statistics literature on instrumental variables methods from an econometrics perspective. I discuss some of the older, economic, applications including supply and demand models and relate them to the recent applications in settings of randomized experiments with noncompliance. I discuss the assumptions underlying instrumental variables methods and in what settings these may be plausible. By providing context to the current applications a better understanding of the applicability of these methods may arise.

Suggested Citation

  • Imbens, Guido W., 2014. "Instrumental Variables: An Econometrician's Perspective," IZA Discussion Papers 8048, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp8048
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    Cited by:

    1. Bischoff, Oliver & Buchwald, Achim, 2015. "Horizontal and Vertical Firm Networks, Corporate Performance and Product Market Competition," MPRA Paper 63413, University Library of Munich, Germany.
    2. Philip DeCicca & Donald Kenkel & Feng Liu, 2015. "Reservation Prices: An Economic Analysis of Cigarette Purchases on Indian Reservations," National Tax Journal, National Tax Association, vol. 68(1), pages 93-118, March.
    3. Michel Dumont, 2015. "Working Paper 05-15 - Evaluation of federal tax incentives for private R&D in Belgium: An update," Working Papers 1505, Federal Planning Bureau, Belgium.
    4. Frank Windmeijer & Helmut Farbmacher & Neil Davies & George Davey Smith, 2016. "On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments," Bristol Economics Discussion Papers 16/674, Department of Economics, University of Bristol, UK, revised 08 Aug 2017.
    5. Òscar Jordà & Moritz Schularick & Alan M. Taylor, 2017. "The effects of quasi-random monetary experiments," NBER Working Papers 23074, National Bureau of Economic Research, Inc.
    6. Jorda, Oscar & Schularick, Moritz & Taylor, Alan M., 2017. "Large and State-Dependent Effects of Quasi-Random Monetary Experiments," Working Paper Series 2017-2, Federal Reserve Bank of San Francisco.
    7. Battey, Heather & Feng, Qiang & Smith, Richard J., 2016. "Improving confidence set estimation when parameters are weakly identified," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 117-123.
    8. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    9. Vassili Bazinas & Bent Nielsen, 2015. "Causal transmission in reduced-form models," Economics Papers 2015-W07, Economics Group, Nuffield College, University of Oxford.
    10. Qin, Duo, 2014. "Resurgence of instrument variable estimation and fallacy of endogeneity," Economics Discussion Papers 2014-42, Kiel Institute for the World Economy (IfW).
    11. Ertefaie Ashkan & Small Dylan & Flory James & Hennessy Sean, 2016. "Selection Bias When Using Instrumental Variable Methods to Compare Two Treatments But More Than Two Treatments Are Available," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 219-232, May.
    12. Blaise Melly und Kaspar Wüthrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.
    13. Federico Crudu & Giovanni Mellace & Zsolt Sandor, 2017. "Inference in instrumental variables models with heteroskedasticity and many instruments," Department of Economics University of Siena 761, Department of Economics, University of Siena.
    14. Bischoff, Oliver & Buchwald, Achim, 2016. "Horizontal and Vertical Firm Networks, Corporate Performance and Product Market Competition," Annual Conference 2016 (Augsburg): Demographic Change 145730, Verein für Socialpolitik / German Economic Association.

    More about this item

    Keywords

    simultaneous equations models; randomized experiments; potential outcomes; noncompliance; selection models; instrumental variables;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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