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Document type:
Zeitschriftenaufsatz 
Author(s):
Ramsauer, F.; Min, A.; Lingauer, M. 
Non-TUM Co-author(s):
ja 
Cooperation:
national 
Title:
Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components 
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 laten...    »
 
Keywords:
expectation-maximization algorithm; factor-augmented vector autoregression model; forecast error variance decomposition; impulse response function; incomplete data; Kalman Filter 
Intellectual Contribution:
Discipline-based Research 
Journal title:
Econometrics 
Journal listet in FT50 ranking:
nein 
Year:
2019 
Year / month:
2019-07 
Key publication:
Nein 
Peer reviewed:
Ja 
Commissioned:
not commissioned 
Technology:
Nein 
Interdisciplinarity:
Nein 
Mission statement:
Ethics and Sustainability:
Nein