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Unreliable Inferences About Unobserved Processes: A Critique of Partial Observability Models*

  • Carlisle Rainey and Robert A. Jackson
Abstract

Methodologists and econometricians advocate the partial observability model as a tool that enables researchers to estimate the distinct effects of a single explanatory variable on two partially observable outcome variables. However, we show that when the explanatory variable of interest influences both partially observable outcomes, the partial observability model estimates are extremely sensitive to misspecification. We use Monte Carlo simulations to show that, under partial observability, minor, unavoidable misspecification of the functional form can lead to substantial large-sample bias, even though the same misspecification leads to little or no bias under full observability.

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Carlisle Rainey is an Assistant Professor in the Department of Political Science, Texas A&M University, 2010 Allen Building, College Station, TX 77843 (crainey@tamu.edu). Robert A. Jackson is a Professor in the Department of Political Science, Florida State University, 531 Bellamy Building, Tallahassee, FL 32306 (rjackson@fsu.edu). The authors thank Will Moore, Austin Mitchell, participants at the 2012 Southern Political Science Association Annual Conference, and participants at the 2012 Midwest Political Science Association Annual Conference for valuable comments on previous versions of the manuscript. To view supplementary material for this article, please visit https://doi.org/10.1017/psrm.2017.3

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Political Science Research and Methods
  • ISSN: 2049-8470
  • EISSN: 2049-8489
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