Generated covariates in nonparametric estimation: A short review

Arbeitspapier

In many applications, covariates are not observed but have to be estimated from data. We outline some regression-type models where such a situation occurs and discuss estimation of the regression function in this context.We review theoretical results on how asymptotic properties of nonparametric estimators differ in the presence of generated covariates from the standard case where all covariates are observed. These results also extend to settings where the focus of interest is on average functionals of the regression function.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2012-042

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
• Subject: Nonparametric estimation ; generated covariates ; Varianzanalyse ; Schätztheorie ; Nichtparametrisches Verfahren ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Mammen, Enno ; Rothe, Christoph ; Schienle, Melanie
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (where): Berlin
• (when): 2012

Further information

• Handle: http://hdl.handle.net/10419/79567
• Last update: 10.03.2025, 11:44 AM CET