Computational and statistical efficiency of semiparametric gls estimators

Joel L. Horowitz; George R. Neumann

doi:10.1080/07474938908800172

Computational and statistical efficiency of semiparametric gls estimators

Joel L. Horowitz, George R. Neumann

Economics

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

In this note, we report a dramatic improvement in the computational efficiency of semiparametric generalized least squares(SGLS) estimation. Computation of SGLS estimates no longer presents serious problems with data sets of moderate size. We also correct a numerical error in the standard errors of the SGLS estimates reported in our recent paper in this journal (Horowitz and Neumann, 1987). The corrected standard errors of SGLS are comparable to those we reported for quantile estimates.

Original language	English
Pages (from-to)	223-225
Journal	Econometric Reviews
Volume	8
DOIs	https://doi.org/10.1080/07474938908800172
State	Published - 1989

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title = "Computational and statistical efficiency of semiparametric gls estimators",

abstract = "In this note, we report a dramatic improvement in the computational efficiency of semiparametric generalized least squares(SGLS) estimation. Computation of SGLS estimates no longer presents serious problems with data sets of moderate size. We also correct a numerical error in the standard errors of the SGLS estimates reported in our recent paper in this journal (Horowitz and Neumann, 1987). The corrected standard errors of SGLS are comparable to those we reported for quantile estimates.",

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T1 - Computational and statistical efficiency of semiparametric gls estimators

AU - Horowitz, Joel L.

AU - Neumann, George R.

PY - 1989

Y1 - 1989

N2 - In this note, we report a dramatic improvement in the computational efficiency of semiparametric generalized least squares(SGLS) estimation. Computation of SGLS estimates no longer presents serious problems with data sets of moderate size. We also correct a numerical error in the standard errors of the SGLS estimates reported in our recent paper in this journal (Horowitz and Neumann, 1987). The corrected standard errors of SGLS are comparable to those we reported for quantile estimates.

AB - In this note, we report a dramatic improvement in the computational efficiency of semiparametric generalized least squares(SGLS) estimation. Computation of SGLS estimates no longer presents serious problems with data sets of moderate size. We also correct a numerical error in the standard errors of the SGLS estimates reported in our recent paper in this journal (Horowitz and Neumann, 1987). The corrected standard errors of SGLS are comparable to those we reported for quantile estimates.

U2 - 10.1080/07474938908800172

DO - 10.1080/07474938908800172

M3 - Article

SN - 0747-4938

VL - 8

SP - 223

EP - 225

JO - Econometric Reviews

JF - Econometric Reviews

ER -

Computational and statistical efficiency of semiparametric gls estimators

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