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Factor augmented VAR revisited - A sparse dynamic factor model approach

Sylvia Kaufmann () and Simon Beyeler

Annual Conference 2018 (Freiburg, Breisgau): Digital Economy from Verein für Socialpolitik / German Economic Association

Abstract: 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.

Keywords: Bayesian FAVAR; sparsity; factor identification (search for similar items in EconPapers)
JEL-codes: C32 C55 E32 E43 E52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-mac
Date: 2018
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https://www.econstor.eu/bitstream/10419/181602/1/VfS-2018-pid-13831.pdf (application/pdf)

Related works:
Working Paper: Factor augmented VAR revisited - A sparse dynamic factor model approach (2019) Downloads
Working Paper: Factor augmented VAR revisited - A sparse dynamic factor model approach (2016) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc18:181602

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