Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247531 
Year of Publication: 
2019
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 7 [Issue:] 3 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-43
Publisher: 
MDPI, Basel
Abstract: 
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 data. In contrast to the existing literature, we do not require observable factor components to be part of the panel data. For this purpose, we modify the Kalman Filter for factors consisting of latent and observed components, which significantly improves the reconstruction of latent factors according to the performed simulation study. To identify model parameters uniquely, the loadings matrix is constrained. In our empirical application, the presented framework analyzes US data for measuring the effects of the monetary policy on the real economy and financial markets. Here, the consequences for the quarterly Gross Domestic Product (GDP) growth rates are of particular importance.
Subjects: 
forecast error variance decomposition
expectation-maximization algorithm
factor-augmented vector autoregression model
impulse response function
incomplete data
Kalman Filter
JEL: 
C33
E44
E52
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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