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Estimating covariance matrices using estimating functions in nonparametric and semiparametric regression

Author

Listed:
  • Carroll, Raymond J.
  • Iturria, Stephen J.
  • Gutierrez, Roberto G.

Abstract

We use ideas from estimating function theory to derive new, simply computed consistent covariance matrix estimates in nonparametric regression and in a class of semiparametric problems. Unlike other estimates in the literature, ours do not require auxiliary or additional nonparametric regressions.

Suggested Citation

  • Carroll, Raymond J. & Iturria, Stephen J. & Gutierrez, Roberto G., 1997. "Estimating covariance matrices using estimating functions in nonparametric and semiparametric regression," SFB 373 Discussion Papers 1997,14, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    • Handle: RePEc:zbw:sfb373:199714
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    Cited by:

    1. Takayuki Toda & Ayako Wakano & Takahiro Hoshino, 2019. "Regression Discontinuity Design with Multiple Groups for Heterogeneous Causal Effect Estimation," Papers 1905.04443, arXiv.org.

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