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An empirical comparison between a regression framework and the Synthetic Control Method

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  • Gharehgozli, Orkideh

Abstract

The Synthetic Control Method has been used in comparative case studies in which the existence of a counterfactual unit with a high level of similarities and comparability is crucial. On the other hand, while many methods have been developed to enhance our estimation power, not many studies have explored the prediction power of the traditional regression frameworks in such comparative case studies. In this paper, we empirically compare the Synthetic Control Method with a Dynamic Panel Data Regression Framework. We compare the estimation result and the prediction power of the predicted unit driven from the Dynamic Panel Data model and the counterfactual unit from the SCM. To apply the idea, we employ the recent sanctions on Iran as a suitable case of policy intervention and a comparative case study.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:quaeco:v:81:y:2021:i:c:p:70-81
    DOI: 10.1016/j.qref.2021.05.002
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    as
    1. Sylvia Allegretto & Arindrajit Dube & Michael Reich & Ben Zipperer, 2017. "Credible Research Designs for Minimum Wage Studies," ILR Review, Cornell University, ILR School, vol. 70(3), pages 559-592, May.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. George J. Borjas, 2021. "The Wage Impact Of The Marielitos: A Reappraisal," World Scientific Book Chapters, in: Foundational Essays in Immigration Economics, chapter 12, pages 375-408, World Scientific Publishing Co. Pte. Ltd..
    4. Card, David & Krueger, Alan B, 1994. "Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania," American Economic Review, American Economic Association, vol. 84(4), pages 772-793, September.
    5. Pedroni, Peter, 2004. "Panel Cointegration: Asymptotic And Finite Sample Properties Of Pooled Time Series Tests With An Application To The Ppp Hypothesis," Econometric Theory, Cambridge University Press, vol. 20(3), pages 597-625, June.
    6. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 251-299.
    7. Jörg Breitung & Samarjit Das, 2005. "Panel unit root tests under cross‐sectional dependence," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 59(4), pages 414-433, November.
    8. John J. Donohue & Abhay Aneja & Kyle D. Weber, 2019. "Right‐to‐Carry Laws and Violent Crime: A Comprehensive Assessment Using Panel Data and a State‐Level Synthetic Control Analysis," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 16(2), pages 198-247, June.
    9. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    10. Scott Cunningham & Manisha Shah, 2018. "Decriminalizing Indoor Prostitution: Implications for Sexual Violence and Public Health," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(3), pages 1683-1715.
    11. Nikolay Doudchenko & Guido W. Imbens, 2016. "Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis," NBER Working Papers 22791, National Bureau of Economic Research, Inc.
    12. Kao, Chihwa, 1999. "Spurious regression and residual-based tests for cointegration in panel data," Journal of Econometrics, Elsevier, vol. 90(1), pages 1-44, May.
    13. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    14. Arellano, Manuel, 1989. "A note on the Anderson-Hsiao estimator for panel data," Economics Letters, Elsevier, vol. 31(4), pages 337-341, December.
    15. Peter Pedroni, 1999. "Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 653-670, November.
    16. repec:bla:obuest:v:61:y:1999:i:0:p:631-52 is not listed on IDEAS
    17. Alberto Abadie & Guido W. Imbens, 2002. "Simple and Bias-Corrected Matching Estimators for Average Treatment Effects," NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.
    18. Stephen Bond, 2002. "Dynamic panel data models: a guide to microdata methods and practice," CeMMAP working papers CWP09/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
    20. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    21. Choi, In, 2001. "Unit root tests for panel data," Journal of International Money and Finance, Elsevier, vol. 20(2), pages 249-272, April.
    22. Javier Gardeazabal & Ainhoa Vega‐Bayo, 2017. "An Empirical Comparison Between the Synthetic Control Method and HSIAO et al.'s Panel Data Approach to Program Evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(5), pages 983-1002, August.
    23. Michael Lechner, 1999. "Nonparametric bounds on employment and income effects of continuous vocational training in East Germany," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 1-28.
    24. Li, Kathleen T. & Bell, David R., 2017. "Estimation of average treatment effects with panel data: Asymptotic theory and implementation," Journal of Econometrics, Elsevier, vol. 197(1), pages 65-75.
    25. Lechner, Michael, 1999. "Earnings and Employment Effects of Continuous Off-the-Job Training in East Germany after Unification," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 74-90, January.
    26. Susan Athey & Guido W. Imbens, 2006. "Identification and Inference in Nonlinear Difference-in-Differences Models," Econometrica, Econometric Society, vol. 74(2), pages 431-497, March.
    27. Wan, Shui-Ki & Xie, Yimeng & Hsiao, Cheng, 2018. "Panel data approach vs synthetic control method," Economics Letters, Elsevier, vol. 164(C), pages 121-123.
    28. Giovanni Peri & Vasil Yasenov, 2019. "The Labor Market Effects of a Refugee Wave: Synthetic Control Method Meets the Mariel Boatlift," Journal of Human Resources, University of Wisconsin Press, vol. 54(2), pages 267-309.
    29. Lijphart, Arend, 1971. "Comparative Politics and the Comparative Method," American Political Science Review, Cambridge University Press, vol. 65(3), pages 682-693, September.
    30. Kaddour Hadri, 2000. "Testing for stationarity in heterogeneous panel data," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 148-161.
    31. Jaroslava Hlouskova & Martin Wagner, 2006. "The Performance of Panel Unit Root and Stationarity Tests: Results from a Large Scale Simulation Study," Econometric Reviews, Taylor & Francis Journals, vol. 25(1), pages 85-116.
    32. Joakim Westerlund, 2005. "New Simple Tests for Panel Cointegration," Econometric Reviews, Taylor & Francis Journals, vol. 24(3), pages 297-316.
    33. 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.
    34. repec:bla:obuest:v:61:y:1999:i:0:p:653-70 is not listed on IDEAS
    35. 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.
    36. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    37. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    38. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2020. "Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis," Econometrica, Econometric Society, vol. 88(1), pages 265-296, January.
    39. Alberto Abadie & Alexis Diamond & Jens Hainmueller, 2015. "Comparative Politics and the Synthetic Control Method," American Journal of Political Science, John Wiley & Sons, vol. 59(2), pages 495-510, February.
    40. Stephen R. Bond, 2002. "Dynamic panel data models: a guide to micro data methods and practice," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 1(2), pages 141-162, August.
    41. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    42. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    43. Cheng Hsiao & H. Steve Ching & Shui Ki Wan, 2012. "A Panel Data Approach For Program Evaluation: Measuring The Benefits Of Political And Economic Integration Of Hong Kong With Mainland China," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 705-740, August.
    44. David Roodman, 2006. "How to Do xtabond2," North American Stata Users' Group Meetings 2006 8, Stata Users Group.
    45. 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.
    46. Harris, Richard D. F. & Tzavalis, Elias, 1999. "Inference for unit roots in dynamic panels where the time dimension is fixed," Journal of Econometrics, Elsevier, vol. 91(2), pages 201-226, August.
    47. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    48. G. S. Maddala & Shaowen Wu, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 631-652, November.
    49. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    50. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
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    More about this item

    Keywords

    Synthetic Control Method; Dynamic Panel Data; Treatment effect; Counterfactual; Comparative case study;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • F5 - International Economics - - International Relations, National Security, and International Political Economy
    • F4 - International Economics - - Macroeconomic Aspects of International Trade and Finance

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