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Volatile pies: Modeling compositional volatility

Author

Listed:
  • Abbie Eastman
  • Andrea Junqueira
  • Ali Kagalwala
  • Andrew Q. Philips
  • Guy D. Whitten

Abstract

Objective The study aims to demonstrate the utility of modeling compositional volatility in substantive domains beyond budgeting. Methods We show how to model compositional volatility on its own or as a part of a system of equations in which the component parts of the compositional outcome variable are also modeled. Results Using data on the volatility of support for German political parties, we demonstrate the usefulness of stand‐alone models of compositional volatility. Using data on the volatility of income shares in the United States, we demonstrate the usefulness of modeling volatility together with compositional components. Conclusion There is considerable potential for modeling compositional volatility.

Suggested Citation

  • Abbie Eastman & Andrea Junqueira & Ali Kagalwala & Andrew Q. Philips & Guy D. Whitten, 2024. "Volatile pies: Modeling compositional volatility," Social Science Quarterly, Southwestern Social Science Association, vol. 105(4), pages 965-979, July.
    • Handle: RePEc:bla:socsci:v:105:y:2024:i:4:p:965-979
    DOI: 10.1111/ssqu.13406
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    References listed on IDEAS

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    1. Yoo Sun Jung & Flávio D. S. Souza & Andrew Q. Philips & Amanda Rutherford & Guy D. Whitten, 2020. "A command to estimate and interpret models of dynamic compositional dependent variables: New features for dynsimpie," Stata Journal, StataCorp LP, vol. 20(3), pages 584-603, September.
    2. Ali Kagalwala & Andrew Q. Philips & Guy D. Whitten, 2021. "What About the Rest of the Pie? A Dynamic Compositional Approach to Modeling Inequality," Social Science Quarterly, Southwestern Social Science Association, vol. 102(4), pages 1534-1552, July.
    3. Leblang, David & Mukherjee, Bumba, 2004. "Presidential Elections and the Stock Market: Comparing Markov-Switching and Fractionally Integrated GARCH Models of Volatility," Political Analysis, Cambridge University Press, vol. 12(3), pages 296-322, July.
    4. Tomz, Michael & Wittenberg, Jason & King, Gary, 2003. "Clarify: Software for Interpreting and Presenting Statistical Results," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 8(i01).
    5. Braumoeller, Bear F., 2006. "Explaining Variance; Or, Stuck in a Moment We Can't Get Out Of," Political Analysis, Cambridge University Press, vol. 14(3), pages 268-290, July.
    6. Tomz, Michael & Tucker, Joshua A. & Wittenberg, Jason, 2002. "An Easy and Accurate Regression Model for Multiparty Electoral Data," Political Analysis, Cambridge University Press, vol. 10(1), pages 66-83, January.
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