NDLI: Identification and Nonparametric Estimation of a Transformed Additively Separable Model

Content Provider	Semantic Scholar
Author	Jacho-Chávez, David T. Lewbel, Arthur Linton, Oliver
Copyright Year	2006
Abstract	Let r(x,z) be a function that, along with its derivatives, can be consistently estimated nonparametrically. This paper discusses the identification and consistent estimation of the unknown functions H, M, G and F, where r(x,z)=H[M(x,z)], M(x,z)=G(x)+F(z), and H is strictly monotonic. An estimation algorithm is proposed for each of the model's unknown components when r(x,z) represents a conditional mean function. The resulting estimators use marginal integration to separate the components G and F. Our estimators are shown to have a limiting Normal distribution with a faster rate of convergence than unrestricted nonparametric alternatives. Their small sample performance is studied in a Monte Carlo experiment. We apply our results to estimate generalized homothetic production functions for four industries in the Chinese economy.
Starting Page	392
Ending Page	407
Page Count	16
File Format	PDF HTM / HTML
DOI	10.1016/j.jeconom.2009.11.008
Volume Number	156
Alternate Webpage(s)	https://www2.bc.edu/~lewbel/JLL.pdf
Alternate Webpage(s)	http://fmwww.bc.edu/ec-p/wp652.pdf
Alternate Webpage(s)	http://www2.bc.edu/~lewbel/JLL.pdf
Alternate Webpage(s)	http://www2.bc.edu/~lewbel/JLL14.pdf
Alternate Webpage(s)	http://www2.bc.edu/~lewbel/JLL9.pdf
Alternate Webpage(s)	https://hal.archives-ouvertes.fr/hal-00646004/document
Alternate Webpage(s)	https://www2.bc.edu/~lewbel/JLL9.pdf
Alternate Webpage(s)	https://dlib.bc.edu/islandora/object/bc-ir:103226/datastream/PDF/download/citation.pdf
Alternate Webpage(s)	https://www2.bc.edu/arthur-lewbel/JLL.pdf
Alternate Webpage(s)	https://www.bc.edu/content/dam/files/schools/cas_sites/economics/pdf/workingpapers/wp652.pdf
Alternate Webpage(s)	https://www2.bc.edu/arthur-lewbel/JLL9.pdf
Alternate Webpage(s)	https://doi.org/10.1016/j.jeconom.2009.11.008
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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