Average Derivative Estimation with Missing Responses

Francesco Bravo, Kim Huynh and David Jacho-Chávez

A chapter in Missing Data Methods: Cross-sectional Methods and Applications, 2011, pp 129-154 from Emerald Group Publishing Limited

Abstract: This chapter proposes a simple procedure to estimate average derivatives in nonparametric regression models with incomplete responses. The method consists of replacing the responses with an appropriately weighted version and then use local polynomial estimation for the average derivatives. The resulting estimator is shown to be asymptotically normal, and an estimator of its asymptotic variance–covariance matrix is also shown to be consistent. Monte Carlo experiments show that the proposed estimator has desirable finite sample properties.

Keywords: Local polynomial estimation; average derivatives; missing at random; partial index model; random censoring (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-9053(2011)000027a008

DOI: 10.1108/S0731-9053(2011)000027A008

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