[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/spt/stecon/v9y2020i1f9_1_4.html
   My bibliography  Save this article

A Monte Carlo Analysis of Robustness of the Synthetic Control Method and Dynamic Panel Estimation: A Comparative Case Study of a Policy Intervention

Author

Listed:
  • Orkideh Gharehgozli
Abstract
In comparative case studies, by solving an optimization problem, the synthetic control method provides a point estimate for an intervention effect and it suffers from lack of considering an asymptotic distribution of the estimator. On the other hand, we can benefit from such considerations while working with a regression framework; and many studies have been done and many methods have been offered in order to overcome the potential shortages of a traditional regression framework in such case studies. In this paper, we use Monte Carlo simulation to compare the robustness and sensitivity between the synthetic control method and a dynamic panel data regression framework. Empirical work in based on a suitable case of a policy intervention and a comparative case study: sanctions on Iran. We conclude that the dynamic panel data model seems to be performing well with the macro level aggregate data and a comparative case study scenario, and the assumptions are appropriate. However, for the synthetic control method we observe large standard errors in the estimated values which result in insignificance of the point estimates. We also take advantage of the replicated trials, and we analyze and compare the sensitivity of the synthetic control method and the dynamic panel data model to the choice of the donor pool and the treatment assignment. JEL classification numbers: C15, C33, C5

Suggested Citation

  • Orkideh Gharehgozli, 2020. "A Monte Carlo Analysis of Robustness of the Synthetic Control Method and Dynamic Panel Estimation: A Comparative Case Study of a Policy Intervention," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 9(1), pages 1-4.
    • Handle: RePEc:spt:stecon:v:9:y:2020:i:1:f:9_1_4
    as

    Download full text from publisher

    File URL: http://www.scienpress.com/Upload/JSEM%2fVol%209_1_4.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    2. Gharehgozli, Orkideh, 2017. "An estimation of the economic cost of recent sanctions on Iran using the synthetic control method," Economics Letters, Elsevier, vol. 157(C), pages 141-144.
    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. Gharehgozli, Orkideh, 2021. "An empirical comparison between a regression framework and the Synthetic Control Method," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 70-81.

    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. Barseghyan, Gayane, 2019. "Sanctions and counter-sanctions : What did they do?," BOFIT Discussion Papers 24/2019, Bank of Finland, Institute for Economies in Transition.
    2. López-Cazar, Ibeth & Papyrakis, Elissaios & Pellegrini, Lorenzo, 2021. "The Extractive Industries Transparency Initiative (EITI) and corruption in Latin America: Evidence from Colombia, Guatemala, Honduras, Peru, and Trinidad and Tobago," Resources Policy, Elsevier, vol. 70(C).
    3. Hyejin Kim & Jungmin Lee, 2020. "The Economic Costs of Diplomatic Conflict," Working Papers 2020-25, Economic Research Institute, Bank of Korea.
    4. Dario Laudati & M. Hashem Pesaran, 2023. "Identifying the effects of sanctions on the Iranian economy using newspaper coverage," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 271-294, April.
    5. Gharehgozli, Orkideh, 2021. "An empirical comparison between a regression framework and the Synthetic Control Method," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 70-81.
    6. David Gilchrist & Thomas Emery & Nuno Garoupa & Rok Spruk, 2023. "Synthetic Control Method: A tool for comparative case studies in economic history," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 409-445, April.
    7. Morteza Ghomi, 2022. "Who is afraid of sanctions? The macroeconomic and distributional effects of the sanctions against Iran," Economics and Politics, Wiley Blackwell, vol. 34(3), pages 395-428, July.
    8. Vincenzo Bove & Jessica Di Salvatore & Roberto Nisticò, 2023. "Economic Sanctions and Trade Flows in the Neighborhood," Journal of Law and Economics, University of Chicago Press, vol. 66(4), pages 671-697.
    9. Frants, Marina, 2024. "Measuring the impact of economic sanctions on the Russian economy using the synthetic control group method," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 74, pages 104-123.
    10. Wu, Rongxin & Tan, Zhizhou & Lin, Boqiang, 2023. "Does carbon emission trading scheme really improve the CO2 emission efficiency? Evidence from China's iron and steel industry," Energy, Elsevier, vol. 277(C).
    11. Chu, Shuai & Liu, Xiangbo, 2021. "Do research universities boost regional economic development? - Evidence from China," GLO Discussion Paper Series 748, Global Labor Organization (GLO).
    12. Mohammad Reza Farzanegan, 2019. "The Effects of International Sanctions on Military Spending of Iran: A Synthetic Control Analysis," CESifo Working Paper Series 7937, CESifo.
    13. Lluc Puig-Codina & Jaime Pinilla & Jaume Puig-Junoy, 2021. "The impact of taxing sugar-sweetened beverages on cola purchasing in Catalonia: an approach to causal inference with time series cross-sectional data," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(1), pages 155-168, February.
    14. Mohammad Reza Farzanegan, 2019. "The Opportunity Cost of the Islamic Revolution and War for Iran," MAGKS Papers on Economics 201929, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    15. Daniel Albalate & Germà Bel, 2018. "“Do government formation deadlocks damage economic growth? Evidence from history’s longest period of political deadlock”," IREA Working Papers 201817, University of Barcelona, Research Institute of Applied Economics, revised Jul 2018.
    16. Bidisha Lahiri & Anurag Deb, 2022. "Impact of the Indian “demonetization” policy on its export performance," Empirical Economics, Springer, vol. 62(6), pages 2799-2825, June.
    17. Hyejin Kim & Jungmin Lee, 2021. "The Economic Costs of Diplomatic Conflict: Evidence from the South Korea–China THAAD Dispute," Korean Economic Review, Korean Economic Association, vol. 37, pages 225-262.
    18. Daniel Aparicio-Pérez & Maria Teresa Balaguer-Coll & Emili Tortosa-Ausina, 2021. "Politics against Economics: The Case of Spanish Regional Financing," Working Papers 2021/15, Economics Department, Universitat Jaume I, Castellón (Spain).
    19. Zheng, Shanshan & Wang, Derek D., 2024. "The local economic impacts of mega nuclear accident: A synthetic control analysis of Fukushima," Economic Modelling, Elsevier, vol. 136(C).
    20. Mohammad Reza Farzanegan & Sven Fischer, 2023. "The Impact of a Large-Scale Natural Disaster on Local Economic Activity: Evidence from the 2003 Bam Earthquake in Iran," CESifo Working Paper Series 10502, CESifo.

    More about this item

    Keywords

    Synthetic Control Method; Panel Data Model; Monte Carlo Simulation; Comparison;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    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:spt:stecon:v:9:y:2020:i:1:f:9_1_4. 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: Eleftherios Spyromitros-Xioufis (email available below). General contact details of provider: http://www.scienpress.com/ .

    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.