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Nonparametric Generalized Least Squares in Applied Regression Analysis
Author(s) -
O'Hara Michael,
Parmeter Christopher F.
Publication year - 2013
Publication title -
pacific economic review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.34
H-Index - 33
eISSN - 1468-0106
pISSN - 1361-374X
DOI - 10.1111/1468-0106.12038
Subject(s) - heteroscedasticity , nonparametric statistics , estimator , econometrics , parametric statistics , generalized least squares , least squares function approximation , mathematics , statistics , nonparametric regression , function (biology) , evolutionary biology , biology
This paper compares a nonparametric generalized least squares ( NPGLS ) estimator to parametric feasible GLS ( FGLS ) and variants of heteroscedasticity robust standard error estimators ( HRSE ) in an applied setting. NPGLS consistently estimates the unknown scedastic function and produces more efficient parameter estimates than HRSE . We apply these various approaches for handling heteroscedasticity to data on professor rankings obtained from R ate M y P rofessors.com. We find that the statistical significance of key variables differs across seven versions of HRSE , leading to different conclusions, and a standard parametric approach to FGLS suffers from misspecification. NPGLS combines the virtues of both of these parametric approaches.