Quadratic mode regression

Research output: Contribution to journalArticle

41 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
Publication statusPublished - 1993 May 1
Externally publishedYes

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Least Trimmed Squares
Regression
Density Estimation
Covariance matrix
kernel
Upper bound
Kernel Smoothing
Consistent Estimator
Asymptotic distribution
Symmetry
Dependent
Requirements
Least squares
Kernel
Density estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Finance
  • Economics and Econometrics

Cite this

Quadratic mode regression. / Lee, Myoung-jae.

In: Journal of Econometrics, Vol. 57, No. 1-3, 01.05.1993, p. 1-19.

Research output: Contribution to journalArticle

Lee, M 1993, 'Quadratic mode regression', Journal of Econometrics, vol. 57, no. 1-3, pp. 1-19.
Lee, Myoung-jae. / Quadratic mode regression. In: Journal of Econometrics. 1993 ; Vol. 57, No. 1-3. pp. 1-19.
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