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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Ramsauer, F.; Min, A.; Lingauer, M.
Nicht-TUM Koautoren:
ja
Kooperation:
national
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...     »
Stichworte:
expectation-maximization algorithm; factor-augmented vector autoregression model; forecast error variance decomposition; impulse response function; incomplete data; Kalman Filter
Intellectual Contribution:
Discipline-based Research
Zeitschriftentitel:
Econometrics
Journal gelistet in FT50 Ranking:
nein
Jahr:
2019
Jahr / Monat:
2019-07
Volltext / DOI:
doi:10.3390/econometrics7030031
WWW:
https://www.mdpi.com/2225-1146/7/3/31
Urteilsbesprechung:
0
Key publication:
Nein
Peer reviewed:
Ja
commissioned:
not commissioned
Technology:
Nein
Interdisziplinarität:
Nein
Leitbild:
;
Ethics und Sustainability:
Nein
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