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.
Bravo, F., Huynh, K.P. and Jacho-Chávez, D.T. (2011), "Average Derivative Estimation with Missing Responses", Drukker, D.M. (Ed.) Missing Data Methods: Cross-sectional Methods and Applications (Advances in Econometrics, Vol. 27 Part 1), Emerald Group Publishing Limited, Bingley, pp. 129-154. https://doi.org/10.1108/S0731-9053(2011)000027A008Download as .RIS
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