Arbeitspapier
Low-rank approximations of nonseparable panel models
We provide estimation methods for panel nonseparable models based on low-rank factor structure approximations. The factor structures are estimated by matrixcompletion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that the resulting estimators are consistent in large panels, but suffer from approximation and shrinkage biases. We correct these biases using matching and difference-in-difference approaches. Numerical examples and an empirical application to the effect of election day registration on voter turnout in the U.S. illustrate the properties and usefulness of our methods.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP52/20
- Klassifikation
-
Wirtschaft
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Fernández-Val, Iván
Freeman, Hugo
Weidner, Martin
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2020
- DOI
-
doi:10.47004/wp.cem.2020.5220
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Fernández-Val, Iván
- Freeman, Hugo
- Weidner, Martin
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2020
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