Arbeitspapier
Inference in weak factor models
In this paper, we consider statistical inference for high-dimensional approximate factor models. We posit a weak factor structure, in which the factor loading matrix can be sparse and the signal eigenvalues may diverge more slowly than the cross-sectional dimension, N. We propose a novel inferential procedure to decide whether each component of the factor loadings is zero or not, and prove that this controls the false discovery rate (FDR) below a pre-assigned level, while the power tends to unity. This "factor selection" procedure is primarily based on a de-sparsified (or debiased) version of the WF-SOFAR estimator of Uematsu and Yamagata (2020), but is also applicable to the principal component (PC) estimator. After the factor selection, the re-sparsified WFSOFAR and sparsified PC estimators are proposed and their consistency is established. Finite sample evidence supports the theoretical results. We apply our procedure to the FRED-MD macroeconomic and financial data, consisting of 128 series from June 1999 to May 2019. The results strongly suggest the existence of sparse factor loadings and exhibit a clear association of each of the extracted factors with a group of macroeconomic variables. In particular, we find a price factor, housing factor, output and income factor, and a money, credit and stock market factor.
- Sprache
-
Englisch
- Erschienen in
-
Series: ISER Discussion Paper ; No. 1080
- Klassifikation
-
Wirtschaft
- Thema
-
Approximate factor models
Debiased SOFAR estimator
Multiple testing
FDRand Power
Re-sparsification
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Uematsu, Yoshimasa
Yamagata, Takashi
- Ereignis
-
Veröffentlichung
- (wer)
-
Osaka University, Institute of Social and Economic Research (ISER)
- (wo)
-
Osaka
- (wann)
-
2020
- Handle
- Letzte Aktualisierung
-
24.05.3025, 12:41 MESZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Uematsu, Yoshimasa
- Yamagata, Takashi
- Osaka University, Institute of Social and Economic Research (ISER)
Entstanden
- 2020
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