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

Carlisle Rainey and Robert A. Jackson

Political Science Research and Methods, 2018, vol. 6, issue 2, 381-391

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.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cup:pscirm:v:6:y:2018:i:02:p:381-391_00

Access Statistics for this article

More articles in Political Science Research and Methods from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().