IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/108679.html
   My bibliography  Save this paper

True structure change, spurious treatment effect? A novel approach to disentangle treatment effects from structure changes

Author

Listed:
  • Hao, Shiming

Abstract

This paper develops a new flexible approach to disentangle treatment effects from structure changes. It is shown that ignoring prior structure changes or endogenous regime switches in causal inferences will lead to false positive or false negative treatment effects estimations. A difference in difference in difference strategy and a novel approach based on Automatically Auxiliary Regressions (AARs) are designed to separately identify and estimate treatment effects, structure changes effects and endogenous regime switch effects. The new approach has several desirable features. First, it does not need instrument variables to handle endogeneities and it is easy to implement with hardly any technical barriers to the empirical researchers; second, it can be extended to isolate one treatment from other treatments when the outcome is the working of a series of treatments; third, it outperforms other popular competitors in small sample simulations and the biases caused by endogeneities vanish with sample size. The new method is illustrated then in a comparative study of supporting direct destruction theory on the impacts of Hanshin-Awaji earthquake and Schumpeterian creative destruction theory on the impacts of Wenchuan earthquake.

Suggested Citation

  • Hao, Shiming, 2021. "True structure change, spurious treatment effect? A novel approach to disentangle treatment effects from structure changes," MPRA Paper 108679, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:108679
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/108679/1/MPRA_paper_108679.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Horwich, George, 2000. "Economic Lessons of the Kobe Earthquake," Economic Development and Cultural Change, University of Chicago Press, vol. 48(3), pages 521-542, April.
    2. Noy, Ilan, 2009. "The macroeconomic consequences of disasters," Journal of Development Economics, Elsevier, vol. 88(2), pages 221-231, March.
    3. Michelle Alexopoulos & Jon Cohen, 2016. "The Medium Is the Measure: Technical Change and Employment, 1909—1949," The Review of Economics and Statistics, MIT Press, vol. 98(4), pages 792-810, October.
    4. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    5. Charles F. Manski, 2013. "Identification of treatment response with social interactions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-23, February.
    6. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
    7. Bin Chen & Yongmiao Hong, 2012. "Testing for Smooth Structural Changes in Time Series Models via Nonparametric Regression," Econometrica, Econometric Society, vol. 80(3), pages 1157-1183, May.
    8. Li, Yong & Yu, Jun, 2012. "Bayesian hypothesis testing in latent variable models," Journal of Econometrics, Elsevier, vol. 166(2), pages 237-246.
    9. Laurent Gobillon & Thierry Magnac, 2016. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls," The Review of Economics and Statistics, MIT Press, vol. 98(3), pages 535-551, July.
    10. Chang, Yoosoon & Choi, Yongok & Park, Joon Y., 2017. "A new approach to model regime switching," Journal of Econometrics, Elsevier, vol. 196(1), pages 127-143.
    11. Guy Michaels & Ferdinand Rauch & Stephen J. Redding, 2012. "Urbanization and Structural Transformation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(2), pages 535-586.
    12. Eduardo Cavallo & Sebastian Galiani & Ilan Noy & Juan Pantano, 2013. "Catastrophic Natural Disasters and Economic Growth," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1549-1561, December.
    13. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
    14. 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.
    15. Fujiki, Hiroshi & Hsiao, Cheng, 2015. "Disentangling the effects of multiple treatments—Measuring the net economic impact of the 1995 great Hanshin-Awaji earthquake," Journal of Econometrics, Elsevier, vol. 186(1), pages 66-73.
    16. Deng, Guoying & Gan, Li & Hernandez, Manuel A., 2015. "Do natural disasters cause an excessive fear of heights? Evidence from the Wenchuan earthquake," Journal of Urban Economics, Elsevier, vol. 90(C), pages 79-89.
    17. Boucekkine, R. & Pommeret, A. & Prieur, F., 2013. "Optimal regime switching and threshold effects," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2979-2997.
    18. 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.
    19. 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.
    20. Leamer, Edward E, 1983. "Let's Take the Con Out of Econometrics," American Economic Review, American Economic Association, vol. 73(1), pages 31-43, March.
    21. Caballero, Ricardo J & Hammour, Mohamad L, 1994. "The Cleansing Effect of Recessions," American Economic Review, American Economic Association, vol. 84(5), pages 1350-1368, December.
    22. White, Halbert, 2006. "Time-series estimation of the effects of natural experiments," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 527-566.
    23. Kim, Chang-Jin, 2009. "Markov-switching models with endogenous explanatory variables II: A two-step MLE procedure," Journal of Econometrics, Elsevier, vol. 148(1), pages 46-55, January.
    24. Sun, Liyang & Abraham, Sarah, 2021. "Estimating dynamic treatment effects in event studies with heterogeneous treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 175-199.
    25. Seidl, Andrea, 2019. "Zeno points in optimal control models with endogenous regime switching," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 353-368.
    26. Lewis, Joshua & Severnini, Edson, 2020. "Short- and long-run impacts of rural electrification: Evidence from the historical rollout of the U.S. power grid," Journal of Development Economics, Elsevier, vol. 143(C).
    27. Raddatz, Claudio, 2007. "Are external shocks responsible for the instability of output in low-income countries?," Journal of Development Economics, Elsevier, vol. 84(1), pages 155-187, September.
    28. Taryn Dinkelman, 2011. "The Effects of Rural Electrification on Employment: New Evidence from South Africa," American Economic Review, American Economic Association, vol. 101(7), pages 3078-3108, December.
    29. Boucekkine, R. & Pommeret, A. & Prieur, F., 2013. "Optimal regime switching and threshold effects," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2979-2997.
    30. Hall, Alastair R. & Han, Sanggohn & Boldea, Otilia, 2012. "Inference regarding multiple structural changes in linear models with endogenous regressors," Journal of Econometrics, Elsevier, vol. 170(2), pages 281-302.
    31. Lawrence E. Blume & William A. Brock & Steven N. Durlauf & Rajshri Jayaraman, 2015. "Linear Social Interactions Models," Journal of Political Economy, University of Chicago Press, vol. 123(2), pages 444-496.
    32. Kim, C.-J.Chang-Jin, 2004. "Markov-switching models with endogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 122(1), pages 127-136, September.
    33. Hudgens, Michael G. & Halloran, M. Elizabeth, 2008. "Toward Causal Inference With Interference," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 832-842, June.
    34. Heckman, James J., 2001. "Econometrics and empirical economics," Journal of Econometrics, Elsevier, vol. 100(1), pages 3-5, January.
    35. Martin Huber & Blaise Melly, 2015. "A Test of the Conditional Independence Assumption in Sample Selection Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1144-1168, November.
    36. Carlos Cinelli & Chad Hazlett, 2020. "Making sense of sensitivity: extending omitted variable bias," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(1), pages 39-67, February.
    37. Johannes Becker & Clemens Fuest, 2010. "Taxing Foreign Profits With International Mergers And Acquisitions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 51(1), pages 171-186, February.
    38. Nadarajah, Saralees, 2006. "On the ratio X/Y for some elliptically symmetric distributions," Journal of Multivariate Analysis, Elsevier, vol. 97(2), pages 342-358, February.
    39. Laura Forastiere & Edoardo M. Airoldi & Fabrizia Mealli, 2021. "Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 901-918, April.
    40. Jenish, Nazgul & Prucha, Ingmar R., 2012. "On spatial processes and asymptotic inference under near-epoch dependence," Journal of Econometrics, Elsevier, vol. 170(1), pages 178-190.
    41. Cecilia Machado, 2017. "Unobserved selection heterogeneity and the gender wage gap," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1348-1366, November.
    42. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory, 2016. "Dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 191(2), pages 276-292.
    43. Mark Skidmore & Hideki Toya, 2002. "Do Natural Disasters Promote Long-Run Growth?," Economic Inquiry, Western Economic Association International, vol. 40(4), pages 664-687, October.
    44. Kim, Chang-Jin & Piger, Jeremy & Startz, Richard, 2008. "Estimation of Markov regime-switching regression models with endogenous switching," Journal of Econometrics, Elsevier, vol. 143(2), pages 263-273, April.
    45. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
    Full references (including those not matched with items on IDEAS)

    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. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    2. Kevin Luo & Tomoko Kinugasa, 2018. "Do natural disasters influence long-term saving?: Assessing the impact of the 2008 Sichuan earthquake on household saving rates using synthetic control," Discussion Papers 1804, Graduate School of Economics, Kobe University.
    3. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    4. Andrii Melnychuk, 2024. "Synthetic Controls with spillover effects: A comparative study," Papers 2405.01645, arXiv.org.
    5. Kirill Borusyak & Xavier Jaravel & Jann Spiess, 2021. "Revisiting Event Study Designs: Robust and Efficient Estimation," Papers 2108.12419, arXiv.org, revised Jan 2024.
    6. Fujiki, Hiroshi & Hsiao, Cheng, 2015. "Disentangling the effects of multiple treatments—Measuring the net economic impact of the 1995 great Hanshin-Awaji earthquake," Journal of Econometrics, Elsevier, vol. 186(1), pages 66-73.
    7. Pantelis Samartsidis & Shaun R. Seaman & Silvia Montagna & André Charlett & Matthew Hickman & Daniela De Angelis, 2020. "A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1437-1459, October.
    8. Huang, Lulu & Liu, Qiannan & Tang, Yugang, 2024. "Long-term economic impact of disasters: Evidence from multiple earthquakes in China," World Development, Elsevier, vol. 174(C).
    9. Giulio Grossi & Marco Mariani & Alessandra Mattei & Patrizia Lattarulo & Ozge Oner, 2020. "Direct and spillover effects of a new tramway line on the commercial vitality of peripheral streets. A synthetic-control approach," Papers 2004.05027, arXiv.org, revised Nov 2023.
    10. 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.
    11. Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2021. "Matrix Completion Methods for Causal Panel Data Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1716-1730, October.
    12. I. Koetsier, 2017. "The fiscal impact of natural disasters," Working Papers 17-17, Utrecht School of Economics.
    13. Bruno Ferman & Cristine Pinto & Vitor Possebom, 2020. "Cherry Picking with Synthetic Controls," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 510-532, March.
    14. Pekka Malo & Juha Eskelinen & Xun Zhou & Timo Kuosmanen, 2024. "Computing Synthetic Controls Using Bilevel Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1113-1136, August.
    15. Emmanuel Apergis & Nicholas Apergis, 2021. "The impact of COVID-19 on economic growth: evidence from a Bayesian Panel Vector Autoregressive (BPVAR) model," Applied Economics, Taylor & Francis Journals, vol. 53(58), pages 6739-6751, December.
    16. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
    17. Heger, Martin Philipp & Neumayer, Eric, 2019. "The impact of the Indian Ocean tsunami on Aceh’s long-term economic growth," Journal of Development Economics, Elsevier, vol. 141(C).
    18. Stefano, Roberta di & Mellace, Giovanni, 2020. "The inclusive synthetic control method," Discussion Papers on Economics 14/2020, University of Southern Denmark, Department of Economics.
    19. Tomasz Serwach, 2022. "The European Union and within-country income inequalities. The case of the New Member States," Working Papers hal-03548416, HAL.
    20. Eli Ben‐Michael & Avi Feller & Jesse Rothstein, 2022. "Synthetic controls with staggered adoption," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 351-381, April.

    More about this item

    Keywords

    structure changes; treatment effects; latent variable; endogeneity; regime switch model; social interactions;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    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:pra:mprapa:108679. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.