Semiparametric estimation with generated covariates

Arbeitspapier

In this paper, we study a general class of semiparametric optimization estimators of a vector-valued parameter. The criterion function depends on two types of infinite-dimensional nuisance parameters: a conditional expectation function that has been estimated nonparametrically using generated covariates, and another estimated function that is used to compute the generated covariates in the first place. We study the asymptotic properties of estimators in this class, which is a nonstandard problem due to the presence of generated covariates. We give conditions under which estimators are root-n consistent and asymptotically normal, and derive a general formula for the asymptotic variance.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2011-064

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: semiparametric estimation ; generated covariates ; profiling ; propensity score ; Nichtparametrisches Verfahren ; Schätztheorie ; Korrelation ; 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): 2011

Further information

• Handle: http://hdl.handle.net/10419/56726
• Last update: 10.03.2025, 11:42 AM CET