Closed-form estimation of nonparametric models with non-classical measurement errors
This paper proposes closed-form estimators for nonparametric regressions using two measurements with non-classical errors. One (administrative) measurement has location-/scale-normalized errors, but the other (survey) measurement has endogenous errors with arbitrary location and scale. For this sett...
Saved in:
Published in | Journal of econometrics Vol. 185; no. 2; pp. 392 - 408 |
---|---|
Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Amsterdam
Elsevier B.V
01.04.2015
Elsevier Sequoia S.A |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | This paper proposes closed-form estimators for nonparametric regressions using two measurements with non-classical errors. One (administrative) measurement has location-/scale-normalized errors, but the other (survey) measurement has endogenous errors with arbitrary location and scale. For this setting of data combination, we derive closed-form identification of nonparametric regressions, and practical closed-form estimators that perform well with small samples. Applying this method to NHANES III, we study how obesity explains health care usage. Clinical measurements and self reports of BMI are used as two measurements with normalized errors and endogenous errors, respectively. We robustly find that health care usage increases with obesity. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0304-4076 1872-6895 |
DOI: | 10.1016/j.jeconom.2014.11.004 |