Arbeitspapier
A simple and efficient estimation method for models with nonignorable missing data
This paper proposes a simple and efficient estimation procedure for the model with non-ignorable missing data studied by Morikawa and Kim (2016). Their semiparametrically efficient estimator requires explicit non- parametric estimation and so suffers from the curse of dimensionality and requires a bandwidth selection. We propose an estimation method based on the Generalized Method of Moments (hereafter GMM). Our method is consistent and asymptotically normal regardless of the number of moments chosen. Furthermore, if the number of moments increases appropriately our estimator can achieve the semiparametric efficiency bound derived in Morikawa and Kim (2016), but under weaker regularity conditions. More- over, our proposed estimator and its consistent covariance matrix are easily computed with the widely available GMM package. We propose two data- based methods for selection of the number of moments. A small scale simulation study reveals that the proposed estimation indeed out-performs the existing alternatives infinite samples.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP02/18
- Klassifikation
-
Wirtschaft
- Thema
-
Nonignorable nonresponse
Generalized method of moments
Semiparametric efficiency
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Ali, Chunrong
Linton, Oliver
Zhang, Zheng
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2018
- DOI
-
doi:10.1920/wp.cem.2018.0218
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 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
- Ali, Chunrong
- Linton, Oliver
- Zhang, Zheng
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2018
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