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A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data

Author

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  • Shor, Boris
  • Bafumi, Joseph
  • Keele, Luke
  • Park, David

Abstract

The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages.

Suggested Citation

  • Shor, Boris & Bafumi, Joseph & Keele, Luke & Park, David, 2007. "A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data," Political Analysis, Cambridge University Press, vol. 15(2), pages 165-181, April.
    • Handle: RePEc:cup:polals:v:15:y:2007:i:02:p:165-181_00
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    Cited by:

    1. Mario Quaranta & Sergio Martini, 2017. "Easy Come, Easy Go? Economic Performance and Satisfaction with Democracy in Southern Europe in the Last Three Decades," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 131(2), pages 659-680, March.
    2. Heidrich, Stefanie, 2015. "Intergenerational Mobility in Sweden: a Regional Perspective," Umeå Economic Studies 916, Umeå University, Department of Economics.
    3. Andrew Bell & Malcolm Fairbrother & Kelvyn Jones, 2019. "Fixed and random effects models: making an informed choice," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 1051-1074, March.
    4. Kyle L Marquardt, 2020. "How and how much does expert error matter? Implications for quantitative peace research," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(6), pages 692-700, November.
    5. Thomas M. Rabovsky & William Curtis Ellis, 2014. "Higher Education and Congressional Influence on Administrative Decisions: An Examination of NSF and NIH Research Grant Funding to Four-Year Universities," Social Science Quarterly, Southwestern Social Science Association, vol. 95(3), pages 740-759, September.
    6. Stijn van Weezel, 2016. "Short term effects of drought on communal conflict in Nigeria," Working Papers 201618, School of Economics, University College Dublin.
    7. Gerald L. McCallister, 2016. "Beyond Dyads: Regional Democratic Strength’s Influence on Dyadic Conflict," International Interactions, Taylor & Francis Journals, vol. 42(2), pages 295-321, March.
    8. Jun Xiang, 2017. "Dyadic Effects, Relevance, and the Empirical Assessment of the Kantian Peace," International Interactions, Taylor & Francis Journals, vol. 43(2), pages 248-271, March.
    9. Hashem Salarzadeh Jenatabadi & Peyman Babashamsi & Datis Khajeheian & Nader Seyyed Amiri, 2016. "Airline Sustainability Modeling: A New Framework with Application of Bayesian Structural Equation Modeling," Sustainability, MDPI, vol. 8(11), pages 1-17, November.
    10. Aaron M. Hoffman & José Kaire, 2020. "Comfortably Numb: Effects of Prolonged Media Coverage," Journal of Conflict Resolution, Peace Science Society (International), vol. 64(9), pages 1666-1692, October.
    11. Dimiter Doychinov Toshkov, 2014. "The dynamic relationship between asylum applications and recognition rates in Europe (1987–2010)," European Union Politics, , vol. 15(2), pages 192-214, June.
    12. Pittau, Maria Grazia & Farcomeni, Alessio & Zelli, Roberto, 2016. "Has the attitude of US citizens towards redistribution changed over time?," Economic Modelling, Elsevier, vol. 52(PB), pages 714-724.
    13. Cai, Zhen & Aguilar, Francisco X., 2013. "Meta-analysis of consumer's willingness-to-pay premiums for certified wood products," Journal of Forest Economics, Elsevier, vol. 19(1), pages 15-31.
    14. Fabrizio Gilardi, 2010. "Who Learns from What in Policy Diffusion Processes?," American Journal of Political Science, John Wiley & Sons, vol. 54(3), pages 650-666, July.
    15. 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.
    16. Stefanie Heidrich, 2017. "Intergenerational mobility in Sweden: a regional perspective," Journal of Population Economics, Springer;European Society for Population Economics, vol. 30(4), pages 1241-1280, October.

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