Abstract
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 RateMyProfessors.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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 456-474 |
| Number of pages | 19 |
| Journal | Pacific Economic Review |
| Volume | 18 |
| Issue number | 4 |
| DOIs | |
| State | Published - Jan 1 2013 |
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ASJC Scopus subject areas
- Economics and Econometrics
Cite this
Nonparametric generalized least squares in applied regression analysis. / O'Hara, Michael; Parmeter, Christopher.
In: Pacific Economic Review, Vol. 18, No. 4, 01.01.2013, p. 456-474.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Nonparametric generalized least squares in applied regression analysis
AU - O'Hara, Michael
AU - Parmeter, Christopher
PY - 2013/1/1
Y1 - 2013/1/1
N2 - 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 RateMyProfessors.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.
AB - 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 RateMyProfessors.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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84885695564&partnerID=8YFLogxK
U2 - 10.1111/1468-0106.12038
DO - 10.1111/1468-0106.12038
M3 - Article
VL - 18
SP - 456
EP - 474
JO - Pacific Economic Review
JF - Pacific Economic Review
SN - 1361-374X
IS - 4
ER -