Quadratic mode regression

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44 Citations (Scopus)

Abstract

Generalizing the mode regression of Lee (1989) with the rectangular kernel (RME), we try a quadratic kernel (QME), smoothing the rectangular kernel. Like RME, QME is the most useful when the dependent variable is truncated. QME is better than RME in that it gives a N 1 2-consistent estimator and an asymptotic distribution which parallels that of Powell's (1986) symmetrically trimmed least squares (STLS). In general, the symmetry requirement of QME is weaker than that of STLS and stronger than that of RME. Estimation of the covariance matrices of both QME and STLS requires density estimation. But a variation of QME can provide an upper bound of the covariance matrix without the burden of density estimation. The upper bound can be made tight at the cost of computation time.

Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalJournal of Econometrics
Volume57
Issue number1-3
DOIs
Publication statusPublished - 1993

ASJC Scopus subject areas

  • Economics and Econometrics

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