Identification and Nonparametric Estimation of a Transformed Additively Separable Model
David Jacho-Chávez (),
Arthur Lewbel () and
Oliver Linton ()
No 652, Boston College Working Papers in Economics from Boston College Department of Economics
Let r(x,z) be a function that, along with its derivatives, can be consistently estimated nonparametrically. This paper discusses identification and consistent estimation of the unknown functions H, M, G and F, where r(x, z) = H[M (x, z)] and M(x,z) = G(x) + F(z). 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, and 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 empirically apply our results to nonparametrically estimate and test generalized homothetic production functions in four industries within the Chinese economy.
Keywords: Partly separable models; Nonparametric regression; Dimension reduction; Generalized homothetic function; Production function. (search for similar items in EconPapers)
JEL-codes: C13 C14 C21 D24 (search for similar items in EconPapers)
Pages: 72 pages
Date: 2006-09-04, Revised 2008-11-26
New Economics Papers: this item is included in nep-ecm and nep-eff
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Journal Article: Identification and nonparametric estimation of a transformed additively separable model (2010)
Working Paper: Identification and nonparametric estimation of a transformed additively separable model (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:bocoec:652
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