December 2020

Latent Variables Analysis in Structural Models: A New Decomposition of the Kalman Smoother

Hess Chung, Cristina Fuentes-Albero, Matthias Paustian, and Damjan Pfajfar

Abstract:

This paper advocates chaining the decomposition of shocks into contributions from forecast errors to the shock decomposition of the latent vector to better understand model inference about latent variables. Such a double decomposition allows us to gauge the inuence of data on latent variables, like the data decomposition. However, by taking into account the transmission mechanisms of each type of shock, we can highlight the economic structure underlying the relationship between the data and the latent variables. We demonstrate the usefulness of this approach by detailing the role of observable variables in estimating the output gap in two models.

Keywords: Kalman smoother, latent variables, shock decomposition, data decomposition, double decomposition

DOI: https://doi.org/10.17016/FEDS.2020.100

PDF: Full Paper

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Last Update: December 04, 2020