IDEAS home Printed from https://ideas.repec.org/p/usg/dp2002/2002-11.html
   My bibliography  Save this paper

Identification of Dynamic Treatment Effects by Instrumental Variables

Author

Listed:
  • Ruth Miquel

Abstract

This paper considers the problem of the identification of causal effects using instrumental variables. We are interested in the effects of some treatments on certain outcomes. First, we consider that a participation in a treatment or a program is only possible one time but we have the choice between more than one program. Under a monotonicity condition and an exclusion restriction, pair-wise Local Average Treatment Effects are identifiable. Second, we consider the case where only one program is available but more than one participation is possible, leading to a comparison of sequences of participations (or sequences of programs). In this framework a problem of endogeneity appears: the outcome after one period, affected by the participation in this period, can influence the participation in the next period. Under different versions of the monotonicity condition and the exclusion restriction, identification of the causal effects of sequences of programs are investigated. The introduction of a second period implies a loss of identification for some effects of interest even without any endogeneity problem.

Suggested Citation

  • Ruth Miquel, 2002. "Identification of Dynamic Treatment Effects by Instrumental Variables," University of St. Gallen Department of Economics working paper series 2002 2002-11, Department of Economics, University of St. Gallen.
    • Handle: RePEc:usg:dp2002:2002-11
    as

    Download full text from publisher

    File URL: http://ux-tauri.unisg.ch/RePEc/usg/dp2002/dp0211miquel_ganz.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
    2. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366, Elsevier.
    3. Angrist, J.D. & Imbens, G.W., 1991. "Sources of Identifying Information in Evaluation Models," Harvard Institute of Economic Research Working Papers 1568, Harvard - Institute of Economic Research.
    4. Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460, Elsevier.
    5. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    6. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097, Elsevier.
    7. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    8. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    9. Abadie A., 2002. "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 284-292, March.
    10. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Markus Frölich & Martin Huber, 2017. "Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1645-1666, November.
    2. Ruth Miquel, 2003. "Identification of Effects of Dynamic Treatments with a Difference-in-Differences Approach," University of St. Gallen Department of Economics working paper series 2003 2003-06, Department of Economics, University of St. Gallen.
    3. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    4. 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.
    5. Ravi B. Sojitra & Vasilis Syrgkanis, 2024. "Dynamic Local Average Treatment Effects," Papers 2405.01463, arXiv.org, revised May 2024.
    6. Steven Lehrer & Weili Ding, 2004. "Estimating Dynamic Treatment Effects from Project STAR," Econometric Society 2004 North American Summer Meetings 252, Econometric Society.
    7. Weili Ding & Steven F. Lehrer, 2010. "Estimating Treatment Effects from Contaminated Multiperiod Education Experiments: The Dynamic Impacts of Class Size Reductions," The Review of Economics and Statistics, MIT Press, vol. 92(1), pages 31-42, February.
    8. Viviana Celli, 2022. "Causal mediation analysis in economics: Objectives, assumptions, models," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 214-234, February.
    9. Michael Lechner, 2011. "The Relation of Different Concepts of Causality Used in Time Series and Microeconometrics," Econometric Reviews, Taylor & Francis Journals, vol. 30(1), pages 109-127.

    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. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    2. Jaap Abbring & Gerard Van Den Berg, 2005. "Social experiments and instrumental variables with duration outcomes," IFS Working Papers W05/19, Institute for Fiscal Studies.
    3. James J. Heckman, 2008. "The Principles Underlying Evaluation Estimators with an Application to Matching," Annals of Economics and Statistics, GENES, issue 91-92, pages 9-73.
    4. Joshua D. Angrist, 2004. "Treatment effect heterogeneity in theory and practice," Economic Journal, Royal Economic Society, vol. 114(494), pages 52-83, March.
    5. 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.
    6. Heckman, James J., 2010. "The Assumptions Underlying Evaluation Estimators," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(2), December.
    7. Stefan Boes, 2013. "Nonparametric analysis of treatment effects in ordered response models," Empirical Economics, Springer, vol. 44(1), pages 81-109, February.
    8. Heckman, James, 2001. "Accounting for Heterogeneity, Diversity and General Equilibrium in Evaluating Social Programmes," Economic Journal, Royal Economic Society, vol. 111(475), pages 654-699, November.
    9. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    10. Battistin, Erich & De Nadai, Michele & Sianesi, Barbara, 2014. "Misreported schooling, multiple measures and returns to educational qualifications," Journal of Econometrics, Elsevier, vol. 181(2), pages 136-150.
    11. Christian Volpe Martincus & Jerónimo Carballo, 2010. "Entering new country and product markets: does export promotion help?," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 146(3), pages 437-467, September.
    12. Imbens, Guido W., 2014. "Instrumental Variables: An Econometrician's Perspective," IZA Discussion Papers 8048, Institute of Labor Economics (IZA).
    13. Djebbari, Habiba & Smith, Jeffrey, 2008. "Heterogeneous impacts in PROGRESA," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 64-80, July.
    14. Battistin, Erich & Rettore, Enrico, 2008. "Ineligibles and eligible non-participants as a double comparison group in regression-discontinuity designs," Journal of Econometrics, Elsevier, vol. 142(2), pages 715-730, February.
    15. Belzil, Christian & Hansen, Jörgen, 2005. "A Structural Analysis of the Correlated Random Coefficient Wage Regression Model with an Application to the OLS-IV Puzzle," IZA Discussion Papers 1585, Institute of Labor Economics (IZA).
    16. Cockx, Bart & Bardoulat, Isabelle, 1999. "Vocational Training: Does it speed up the Transition Rate out of Unemployment ?," LIDAM Discussion Papers IRES 1999032, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    17. Ozkan Eren & Serkan Ozbeklik, 2014. "Who Benefits From Job Corps? A Distributional Analysis Of An Active Labor Market Program," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(4), pages 586-611, June.
    18. Pedro H. C. Sant'Anna, 2016. "Program Evaluation with Right-Censored Data," Papers 1604.02642, arXiv.org.
    19. Nguezet, Paul Martin Dontsop & Diagne, Aliou & Okoruwa, Victor Olusegun & Ojehomon, Vivian, 2011. "Impact of Improved Rice Technology (NERICA varieties) on Income and Poverty among Rice Farming Households in Nigeria: A Local Average Treatment Effect (LATE) Approach," Quarterly Journal of International Agriculture, Humboldt-Universitaat zu Berlin, vol. 50(3), pages 1-25.
    20. Erich Battistin & Enrico Rettore, 2003. "Another look at the regression discontinuity design," CeMMAP working papers CWP01/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    More about this item

    Keywords

    Compliers; Local Average Treatment Effect; dynamic treatment regimes; nonparametric identification; instruments;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

    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:usg:dp2002:2002-11. See general information about how to correct material in RePEc.

    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: Joerg Baumberger (email available below). General contact details of provider: https://edirc.repec.org/data/vwasgch.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.