IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v51y2022i2p837-865.html
   My bibliography  Save this article

How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study

Author

Listed:
  • Lars Leszczensky
  • Tobias Wolbring

Abstract

Does X affect Y ? Answering this question is particularly difficult if reverse causality is looming. Many social scientists turn to panel data to address such questions of causal ordering. Yet even in longitudinal analyses, reverse causality threatens causal inference based on conventional panel models. Whereas the methodological literature has suggested various alternative solutions, these approaches face many criticisms, chief among them to be sensitive to the correct specification of temporal lags. Applied researchers are thus left with little guidance. Seeking to provide such guidance, we compare how different panel models perform under a range of different conditions. Our Monte Carlo simulations reveal that unlike conventional panel models, a cross-lagged panel model with fixed effects not only offers protection against bias arising from reverse causality under a wide range of conditions but also helps to circumvent the problem of misspecified temporal lags.

Suggested Citation

  • Lars Leszczensky & Tobias Wolbring, 2022. "How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study," Sociological Methods & Research, , vol. 51(2), pages 837-865, May.
    • Handle: RePEc:sae:somere:v:51:y:2022:i:2:p:837-865
    DOI: 10.1177/0049124119882473
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124119882473
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0049124119882473?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Myoung-jae Lee, 2016. "Generalized Difference in Differences With Panel Data and Least Squares Estimator," Sociological Methods & Research, , vol. 45(1), pages 134-157, February.
    3. Maurice J. G. Bun & Frank Windmeijer, 2010. "The weak instrument problem of the system GMM estimator in dynamic panel data models," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 95-126, February.
    4. Enrique Moral-Benito, 2013. "Likelihood-Based Estimation of Dynamic Panels With Predetermined Regressors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 451-472, October.
    5. Bell, Andrew & Jones, Kelvyn, 2015. "Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data," Political Science Research and Methods, Cambridge University Press, vol. 3(1), pages 133-153, January.
    6. Richard Williams & Paul D. Allison & Enrique Moral-Benito, 2018. "Linear dynamic panel-data estimation using maximum likelihood and structural equation modeling," Stata Journal, StataCorp LP, vol. 18(2), pages 293-326, June.
    7. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    8. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    9. 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.
    10. Kosuke Imai & In Song Kim, 2019. "When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?," American Journal of Political Science, John Wiley & Sons, vol. 63(2), pages 467-490, April.
    11. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
    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. Isabel Dinis, 2023. "Exploring the Drivers of Microregional Agricultural Labor Productivity: Empirical Insights from Portugal," Agriculture, MDPI, vol. 13(11), pages 1-19, November.
    2. James Chu, 2023. "Clarity from Violence? Intragroup Aggression and the Structure of Status Hierarchies," American Sociological Review, , vol. 88(3), pages 454-492, June.
    3. Jaeger, William K. & Kolpin, Van & Siegel, Ryan, 2023. "The environmental Kuznets curve reconsidered," Energy Economics, Elsevier, vol. 120(C).
    4. Ramírez-Rondán, Nelson R. & Rojas-Rojas, Renato M. & Villavicencio, Julio A., 2023. "Political institutions, economic uncertainty and sovereign credit ratings," Finance Research Letters, Elsevier, vol. 53(C).
    5. Alves, Duarte & Alves, Paulo & Carvalho, Luis & Pais, Claudio, 2022. "Cash holdings: International evidence," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    6. Klöcker, J.A. & Daumann, F., 2023. "What drives migration to Germany? A panel data analysis," Research in Economics, Elsevier, vol. 77(2), pages 251-264.
    7. Godwin Kwabla Ekpe & Anna A. Klis, 2023. "Spillover Effects in Irrigated Agriculture from the Groundwater Commons," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 86(3), pages 469-507, November.
    8. Huaccha, Gissell, 2023. "Regional persistence of the energy efficiency gap: Evidence from England and Wales," Energy Economics, Elsevier, vol. 127(PA).
    9. Cantone, Giulio Giacomo, 2023. "The multiversal methodology as a remedy of the replication crisis," MetaArXiv kuhmz, Center for Open Science.
    10. Jahen F. Rezki, 2022. "Political competition and economic performance: evidence from Indonesia," Economics of Governance, Springer, vol. 23(2), pages 83-114, June.
    11. Dong Xuan Nguyen, 2023. "Trade and employment in Vietnam: how firms' trade matters," Asian-Pacific Economic Literature, The Crawford School, The Australian National University, vol. 37(1), pages 20-38, May.
    12. Bojana Vuković & Teodora Tica & Dejan Jakšić, 2022. "Sustainable Growth Rate Analysis in Eastern European Companies," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    13. George Kapetanios & Nora Neuteboom & Feiko Ritsema & Alexia Ventouri, 2022. "How did consumers react to the COVID‐19 pandemic over time?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(5), pages 961-993, October.
    14. Halkawt Ismail Mohammed Amin & Kemal Cek, 2023. "The Effect of Golden Ratio-Based Capital Structure on Firm’s Financial Performance," Sustainability, MDPI, vol. 15(9), pages 1-25, April.

    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. Maria Elena Bontempi & Jan Ditzen, 2023. "GMM-lev estimation and individual heterogeneity: Monte Carlo evidence and empirical applications," Papers 2312.00399, arXiv.org, revised Dec 2023.
    2. Hayakawa, Kazuhiko & Pesaran, M. Hashem, 2015. "Robust standard errors in transformed likelihood estimation of dynamic panel data models with cross-sectional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 188(1), pages 111-134.
    3. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    4. Al-Jahwari, Salim Ahmed Said, 2021. "Does the Twin-Deficits doctrine apply to the Gulf Cooperation Council? A dynamic panel VAR-X model approach," MPRA Paper 111232, University Library of Munich, Germany.
    5. Kripfganz, Sebastian, 2014. "Unconditional Transformed Likelihood Estimation of Time-Space Dynamic Panel Data Models," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100604, Verein für Socialpolitik / German Economic Association.
    6. Kazuhiko Hayakawa & M. Hashem Pesaran, 2012. "Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models," Working Paper series 38_12, Rimini Centre for Economic Analysis.
    7. Biørn, Erik, 2012. "The Measurement Error Problem in Dynamic Panel Data Analysis: Modeling and GMM Estimation," Memorandum 02/2012, Oslo University, Department of Economics.
    8. Kruiniger, Hugo, 2013. "Quasi ML estimation of the panel AR(1) model with arbitrary initial conditions," Journal of Econometrics, Elsevier, vol. 173(2), pages 175-188.
    9. Kim, Hoolda & Mitra, Sophie, 2022. "Dynamics of health and labor income in Korea," The Journal of the Economics of Ageing, Elsevier, vol. 21(C).
    10. Makram El‐Shagi & Steven Yamarik, 2019. "State‐level capital and investment: Refinements and update," Growth and Change, Wiley Blackwell, vol. 50(4), pages 1411-1422, December.
    11. Ilir MITEZA, 2012. "Fiscal Deficits, Current Deficits and Investment: A Panel Causality Framework of 20 OECD countries," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 12(1).
    12. Björn Brey & Matthias S. Hertweck, 2023. "The dynamic effects of monsoon rainfall shocks on agricultural yield, wages, and food prices in India," Scandinavian Journal of Economics, Wiley Blackwell, vol. 125(3), pages 616-654, July.
    13. Roberto Dell'Anno & Adalgiso Amendola, 2015. "Social Exclusion and Economic Growth: An Empirical Investigation in European Economies," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(2), pages 274-301, June.
    14. Petri Rouvinen, 2002. "R&D—Productivity Dynamics: Causality, Lags, and ‘Dry Holes’," Journal of Applied Economics, Taylor & Francis Journals, vol. 5(1), pages 123-156, May.
    15. Zhao, Jun & Jiang, Qingzhe & Dong, Xiucheng & Dong, Kangyin, 2021. "Assessing energy poverty and its effect on CO2 emissions: The case of China," Energy Economics, Elsevier, vol. 97(C).
    16. Fedotenkov, Igor & Idrisov, Georgy, 2021. "A supply-demand model of public sector size," Economic Systems, Elsevier, vol. 45(2).
    17. Bun, Maurice J.G. & Kleibergen, Frank, 2022. "Identification Robust Inference For Moments-Based Analysis Of Linear Dynamic Panel Data Models," Econometric Theory, Cambridge University Press, vol. 38(4), pages 689-751, August.
    18. Fritsch, Markus, 2019. "On GMM estimation of linear dynamic panel data models," Passauer Diskussionspapiere, Betriebswirtschaftliche Reihe B-36-19, University of Passau, Faculty of Business and Economics.
    19. Biørn, Erik & Han, Xuehui, 2012. "Panel Data Dynamics and Measurement Errors: GMM Bias, IV Validity and Model Fit – A Monte Carlo Study," Memorandum 27/2012, Oslo University, Department of Economics.
    20. Arellano, Manuel, 2016. "Modelling optimal instrumental variables for dynamic panel data models," Research in Economics, Elsevier, vol. 70(2), pages 238-261.

    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:sae:somere:v:51:y:2022:i:2:p:837-865. 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: SAGE Publications (email available below). General contact details of provider: .

    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.