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Exploring the Dynamics of Latent Variable Models

Author

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  • Reuning, Kevin
  • Kenwick, Michael R.
  • Fariss, Christopher J.

Abstract

Researchers face a tradeoff when applying latent variable models to time-series, cross-sectional data. Static models minimize bias but assume data are temporally independent, resulting in a loss of efficiency. Dynamic models explicitly model temporal data structures, but smooth estimates of the latent trait across time, resulting in bias when the latent trait changes rapidly. We address this tradeoff by investigating a new approach for modeling and evaluating latent variable estimates: a robust dynamic model. The robust model is capable of minimizing bias and accommodating volatile changes in the latent trait. Simulations demonstrate that the robust model outperforms other models when the underlying latent trait is subject to rapid change, and is equivalent to the dynamic model in the absence of volatility. We reproduce latent estimates from studies of judicial ideology and democracy. For judicial ideology, the robust model uncovers shocks in judicial voting patterns that were not previously identified in the dynamic model. For democracy, the robust model provides more precise estimates of sudden institutional changes such as the imposition of martial law in the Philippines (1972–1981) and the short-lived Saur Revolution in Afghanistan (1978). Overall, the robust model is a useful alternative to the standard dynamic model for modeling latent traits that change rapidly over time.

Suggested Citation

  • Reuning, Kevin & Kenwick, Michael R. & Fariss, Christopher J., 2019. "Exploring the Dynamics of Latent Variable Models," Political Analysis, Cambridge University Press, vol. 27(4), pages 503-517, October.
  • Handle: RePEc:cup:polals:v:27:y:2019:i:04:p:503-517_00
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    Cited by:

    1. Cindy Cheng & Joan Barcelo & Allison Spencer Hartnett & Robert Kubinec & Luca Messerschmidt, 2020. "CoronaNet: A Dyadic Dataset of Government Responses to the COVID-19 Pandemic," Working Papers 20200042, New York University Abu Dhabi, Department of Social Science, revised Apr 2020.
    2. Kobayashi, Yoshiharu & Heinrich, Tobias & Bryant, Kristin A., 2021. "Public support for development aid during the COVID-19 pandemic," World Development, Elsevier, vol. 138(C).
    3. Joan Barceló & Robert Kubinec & Cindy Cheng & Tiril Høye Rahn & Luca Messerschmidt, 2022. "Windows of repression: Using COVID-19 policies against political dissidents?," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(1), pages 73-89, January.
    4. Zhanna Terechshenko, 2020. "Hot under the collar: A latent measure of interstate hostility," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(6), pages 764-776, November.
    5. R Joseph Huddleston, 2020. "Continuous recognition: A latent variable approach to measuring international sovereignty of self-determination movements," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(6), pages 789-800, November.
    6. Jule Krüger & Ragnhild Nordås, 2020. "A latent variable approach to measuring wartime sexual violence," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(6), pages 728-739, November.
    7. Christopher J Fariss & Michael R Kenwick & Kevin Reuning, 2020. "Estimating one-sided-killings from a robust measurement model of human rights," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(6), pages 801-814, November.
    8. Christopher J Fariss & James Lo, 2020. "Innovations in concepts and measurement for the study of peace and conflict," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(6), pages 669-678, November.

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