Identification Through Sparsity in Factor Models

Simon Freyaldenhoven

No 20-25, Working Papers from Federal Reserve Bank of Philadelphia

Abstract: Factor models are generally subject to a rotational indeterminacy, meaning that individual factors are only identified up to a rotation. In the presence of local factors, which only affect a subset of the outcomes, we show that the implied sparsity of the loading matrix can be used to solve this rotational indeterminacy. We further prove that a rotation criterion based on the 1-norm of the loading matrix can be used to achieve identification even under approximate sparsity in the loading matrix. This enables us to consistently estimate individual factors, and to interpret them as structural objects. Monte Carlo simulations suggest that our criterion performs better than widely used heuristics, and we find strong evidence for the presence of local factors in financial and macroeconomic datasets.

Keywords: identification; factor models; sparsity; local factors (search for similar items in EconPapers)
JEL-codes: C38 C55 (search for similar items in EconPapers)
Pages: 43
Date: 2020-06-22
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.philadelphiafed.org/-/media/frbp/asset ... ers/2020/wp20-25.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fip:fedpwp:88229

Ordering information: This working paper can be ordered from

DOI: 10.21799/frbp.wp.2020.25

Access Statistics for this paper

More papers in Working Papers from Federal Reserve Bank of Philadelphia Contact information at EDIRC.
Bibliographic data for series maintained by Beth Paul ().