IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v30y2011i1p109-127.html
   My bibliography  Save this article

The Relation of Different Concepts of Causality Used in Time Series and Microeconometrics

Author

Listed:
  • Michael Lechner

Abstract

Granger and Sims noncausality (GSNC), a concept frequently applied in time series econometrics, is compared to noncausality based on concepts popular in microeconometrics, program evaluation, and epidemiology literature (potential outcome noncausality or PONC). GSNC is defined as a set of restrictions on joint distributions of random variables with observable sample counterparts, whereas PONC combines restrictions on partially unobservable variables (potential outcomes) with different identifying assumptions that relate potential outcome variables to their observable counterparts. Based on the Robins' dynamic model of potential outcomes, we find that in general neither of the concepts implies each other without further (untestable) assumptions. However, the identifying assumptions associated with the sequential selection of the observables link these concepts such that GSNC implies PONC, and vice versa.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:emetrv:v:30:y:2011:i:1:p:109-127
    DOI: 10.1080/07474938.2011.520571
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/07474938.2011.520571
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/07474938.2011.520571?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Michael Lechner & Ruth Miquel, 2010. "Identification of the effects of dynamic treatments by sequential conditional independence assumptions," Empirical Economics, Springer, vol. 39(1), pages 111-137, August.
    2. 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.
    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. Hervé Cardot & Antonio Musolesi, 2017. "Modeling temporal treatment effects with zero inflated semi-parametric regression models: the case of local development policies in France," SEEDS Working Papers 0317, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Aug 2017.
    2. Fiammetta Menchetti & Fabrizio Cipollini & Fabrizia Mealli, 2023. "Combining counterfactual outcomes and ARIMA models for policy evaluation," The Econometrics Journal, Royal Economic Society, vol. 26(1), pages 1-24.
    3. Matthieu Droumaguet & Tomasz Wozniak, 2012. "Bayesian Testing of Granger Causality in Markov-Switching VARs," Economics Working Papers ECO2012/06, European University Institute.
    4. Das, Aniruddha, 2022. "Religious attendance and global cognitive function: A fixed-effects cross-lagged panel modeling study of older U.S. adults," Social Science & Medicine, Elsevier, vol. 292(C).
    5. Matthieu Droumaguet & Anders Warne & Tomasz Wozniak, 2015. "Granger Causality and Regime Inference in Bayesian Markov-Switching VARs," Department of Economics - Working Papers Series 1191, The University of Melbourne.
    6. Ashesh Rambachan & Neil Shephard, 2019. "Econometric analysis of potential outcomes time series: instruments, shocks, linearity and the causal response function," Papers 1903.01637, arXiv.org, revised Feb 2020.

    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. 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.
    2. Michael Lechner & Ruth Miquel & Conny Wunsch, 2011. "Long‐Run Effects Of Public Sector Sponsored Training In West Germany," Journal of the European Economic Association, European Economic Association, vol. 9(4), pages 742-784, August.
    3. Lechner, Michael & Sari, Nazmi, 2015. "Labor market effects of sports and exercise: Evidence from Canadian panel data," Labour Economics, Elsevier, vol. 35(C), pages 1-15.
    4. 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.
    5. Christiane Hinerasky, 2014. "Advances in Training Evaluation - Psychological, Educational, Economic, and Econometric Perspectives on the Kirkpatrick Model," Working Papers Dissertations 14, Paderborn University, Faculty of Business Administration and Economics.
    6. Dettmann, Eva & Becker, Claudia & Schmeißer, Christian, 2010. "Is there a Superior Distance Function for Matching in Small Samples?," IWH Discussion Papers 3/2010, Halle Institute for Economic Research (IWH).
    7. 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.
    8. Lechner Michael & Miquel Ruth & Wunsch Conny, 2007. "The Curse and Blessing of Training the Unemployed in a Changing Economy: The Case of East Germany After Unification," German Economic Review, De Gruyter, vol. 8(4), pages 468-509, December.
    9. d'Agostino, Giorgio & Patriarca, Fabrizio & Pieroni, Luca & Scarlato, Margherita, 2020. "The perverse effects of hiring credits as a place-based policy: Evidence from Southern Italy," MPRA Paper 102240, University Library of Munich, Germany.
    10. Hugo Bodory & Martin Huber & Lukáš Lafférs, 2022. "Evaluating (weighted) dynamic treatment effects by double machine learning [Identification of causal effects using instrumental variables]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 628-648.
    11. 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.
    12. Sari, Nazmi & Lechner, Michael, 2015. "Long-run health effects of sports and exercise in Canada," Economics Working Paper Series 1520, University of St. Gallen, School of Economics and Political Science.
    13. Wang‐Sheng Lee & Sandy Suardi, 2011. "Minimum Wages and Employment: Reconsidering the Use of a Time Series Approach as an Evaluation Tool," British Journal of Industrial Relations, London School of Economics, vol. 49(Supplemen), pages 376-401, July.
    14. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    15. Carlos A. Flores & Alfonso Flores-Lagunes, 2007. "Identification and Estimation of Casual Mechanisms and Net Effects of a Treatment," Working Papers 0706, University of Miami, Department of Economics.
    16. Martin Huber & Michael Lechner & Anthony Strittmatter, 2018. "Direct and indirect effects of training vouchers for the unemployed," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(2), pages 441-463, February.
    17. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 136(2), pages 341-396, February.
    18. Katharina Dengler, 2019. "Effectiveness of sequences of classroom training for welfare recipients: what works best in West Germany?," Applied Economics, Taylor & Francis Journals, vol. 51(1), pages 1-46, January.
    19. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    20. Conny Wunsch & Michael Lechner, 2008. "What Did All the Money Do? On the General Ineffectiveness of Recent West German Labour Market Programmes," Kyklos, Wiley Blackwell, vol. 61(1), pages 134-174, February.

    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:taf:emetrv:v:30:y:2011:i:1:p:109-127. 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: the person in charge (email available below). General contact details of provider: http://www.tandfonline.com/LECR20 .

    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.