Arbeitspapier
Inference for heterogeneous effects using low-rank estimations
We study a panel data model with general heterogeneous effects, where slopes are allowed to be varying across both individuals and times. The key assumption for dimension reduction is that the heterogeneous slopes can be expressed as a factor structure so that the high-dimensional slope matrix is of low-rank, so can be estimated using low-rank regularized regression. Our paper makes an important theoretical contribution on the "post-SVT (singular value thresholding) inference". Formally, we show that the post-SVT inference can be conducted via three steps: (1) apply the nuclear-norm penalized estimation;(2) extract eigenvectors from the estimated low-rank matrices, and (3) run least squares to iteratively estimate the individual and time effect components in the slope matrix. To properly control for the effect of the penalized low-rank estimation, we argue that this procedure should be embedded with "partial out the mean structure" and "sample splitting". The resulting estimators are asymptotically normal and admit valid inferences. Empirically, we apply the proposed methods to estimate the county-level minimum wage effects on the employment.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP31/19
- Klassifikation
-
Wirtschaft
- Thema
-
nuclear norm penalization
post-SVT
sample splitting
interactive effects
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Chernozhukov, Victor
Hansen, Christian Bailey
Liao, Yuan
Zhu, Yinchu
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2019
- DOI
-
doi:10.1920/wp.cem.2019.3119
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Chernozhukov, Victor
- Hansen, Christian Bailey
- Liao, Yuan
- Zhu, Yinchu
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2019
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