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dynsimpie: A command to examine dynamic compositional dependent variables

Author

Listed:
  • Andrew Q. Philips

    (Texas A&M University)

  • Amanda Rutherford

    (Indiana University)

  • Guy D. Whitten

    (Texas A&M University)

Abstract

In this article, we adapt the modeling strategy proposed by Philips, Rutherford, and Whitten (2016, American Journal of Political Science 60: 268– 283) and create a user-friendly Stata command, dynsimpie. This command re- quires the installation of the clarify package of Tomz, Wittenberg, and King (2003, Journal of Statistical Software 8(1): 1–30) and uses the commands in the clarify package to produce estimates from models of compositional dependent variables over time. Users can also examine how counterfactual shocks play through the system with graphs that are easy to interpret. We illustrate this with a model of voter support for the three dominant political parties in the UK. Copyright 2016 by StataCorp LP.

Suggested Citation

  • Andrew Q. Philips & Amanda Rutherford & Guy D. Whitten, 2016. "dynsimpie: A command to examine dynamic compositional dependent variables," Stata Journal, StataCorp LP, vol. 16(3), pages 662-677, September.
    • Handle: RePEc:tsj:stataj:v:16:y:2016:i:3:p:662-667
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    Cited by:

    1. Prelorentzos, Arsenios-Georgios N. & Konstantakis, Konstantinos N. & Michaelides, Panayotis G. & Xidonas, Panos & Goutte, Stephane & Thomakos, Dimitrios D., 2024. "Introducing the GVAR-GARCH model: Evidence from financial markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    2. Adom, Philip Kofi & Amuakwa-Mensah, Franklin & Agradi, Mawunyo Prosper & Nsabimana, Aimable, 2021. "Energy poverty, development outcomes, and transition to green energy," Renewable Energy, Elsevier, vol. 178(C), pages 1337-1352.

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