Konferenzbeitrag
Factor augmented VAR revisited - A sparse dynamic factor model approach
We combine the factor augmented VAR framework with recently developed estimation and identification procedures for sparse dynamic factor models. Working with a sparse hierarchical prior distribution allows us to discriminate between zero and non-zero factor loadings. The non-zero loadings identify the unobserved factors and provide a meaningful economic interpretation for them. Applying our methodology to US macroeconomic data reveals indeed a high degree of sparsity in the data. We use the estimated FAVAR to study the effect of a monetary policy shock and a shock to the term premium. Factors and specific variables show sensible responses to the identified shocks.
- Sprache
-
Englisch
- Erschienen in
-
Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2018: Digitale Wirtschaft - Session: Time Series ; No. D04-V1
- Klassifikation
-
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Large Data Sets: Modeling and Analysis
Business Fluctuations; Cycles
Interest Rates: Determination, Term Structure, and Effects
Monetary Policy
- Thema
-
Bayesian FAVAR
sparsity
factor identification
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Kaufmann, Sylvia
Beyeler, Simon
- Ereignis
-
Veröffentlichung
- (wer)
-
ZBW - Leibniz-Informationszentrum Wirtschaft
- (wo)
-
Kiel, Hamburg
- (wann)
-
2018
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
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Objekttyp
- Konferenzbeitrag
Beteiligte
- Kaufmann, Sylvia
- Beyeler, Simon
- ZBW - Leibniz-Informationszentrum Wirtschaft
Entstanden
- 2018
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