Latent Variables Prognosis in Structural Items: A New Decomposition of the Kalman Smoother
Hess Chung, Cristina Fuentes-Albero, Matthias Paustian, and Damjan Pfajfar
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. This form of double decomposition lets in us to gauge the inuence of files on latent variables, love the files decomposition. However, by taking into legend the transmission mechanisms of every form of wretchedness, we are able to spotlight the economic construction underlying the relationship between the files and the latent variables. We display the usefulness of this approach by detailing the feature of observable variables in estimating the output gap in two units.
Keywords: Kalman smoother, latent variables, shock decomposition, files decomposition, double decomposition
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December 04, 2020