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Identification and Nonparametric Estimation of a Transformed Additively Separable Model

  • David Jacho-Chavez

    (Indiana University)

  • Arthur Lewbel

    ()

    (Boston College)

  • Oliver Linton

    (London School 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.

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Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 652.

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Length: 72 pages
Date of creation: 04 Sep 2006
Date of revision: 26 Nov 2008
Handle: RePEc:boc:bocoec:652
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