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...

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Bibliographic Details
Published inJournal of econometrics Vol. 185; no. 2; pp. 392 - 408
Main Authors Hu, Yingyao, Sasaki, Yuya
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.04.2015
Elsevier Sequoia S.A
Subjects
Body mass index
Closed form
Econometrics
Economic analysis
Economic theory
Error
Estimating techniques
Health care
Measurement errors
Non-classical measurement errors
Nonparametric regressions
Obesity
Regression analysis
Studies
C21
Nonparametric regressions
Non-classical measurement errors
Closed form
C14
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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
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ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2014.11.004