Central Bank of Austria, Economic Studies Division, Otto-Wagner-Platz 3, 1090 Vienna, Austria
The views expressed in this study do not necessarily reflect the official viewpoint of the Oesterreichische Nationalbank or of the Eurosystem.
Econometrics 2019, 7(2), 20; https://doi.org/10.3390/econometrics7020020
Received: 18 December 2018 / Revised: 19 April 2019 / Accepted: 19 April 2019 / Published: 14 May 2019
It is customary to assume that an indicator of a latent variable is driven by the latent variable and some random noise. In contrast, a background indicator is also systematically influenced by variables outside the structural model of interest. Background indicators deserve attention because in empirical work they are difficult to distinguish from ordinary effect indicators. This paper assesses instrumental variable (IV) estimation of the effect of a latent variable in a linear model when a background indicator replaces the latent variable. It turns out that IV estimates are inconsistent in many important cases. In some cases, the estimates capture causal effects of the indicator rather than causal effects of the latent variable. A simulation experiment that considers the impact of economic uncertainty on aggregate consumption illustrates some of the results. View Full-Text
Keywords: causal graph; latent variable; indicator variable; instrumental variable; financial development; stock market volatility►▼ Show Figures
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Raunig, B. Background Indicators. Econometrics 2019, 7, 20.
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Raunig B. Background Indicators. Econometrics. 2019; 7(2):20.Chicago/Turabian Style
Raunig, Burkhard. 2019. "Background Indicators." Econometrics 7, no. 2: 20.
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