Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components

Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components

This article extends the Factor-Augmented Vector Autoregression Model (FAVAR) to mixed-frequency and incomplete panel data. Within the scope of a fully parametric two-step approach, the alternating application of two expectation-maximization algorithms jointly estimates model parameters and missing...

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Bibliographic Details
Main Authors: Franz Ramsauer, Aleksey Min, Michael Lingauer
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Econometrics
Subjects:
expectation-maximization algorithm
factor-augmented vector autoregression model
forecast error variance decomposition
impulse response function
incomplete data
Kalman Filter
Online Access:https://www.mdpi.com/2225-1146/7/3/31

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