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Semiparametric Smooth Coefficient Models

  • Li, Qi, et al
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In this article, we propose a semiparametric smooth coefficient model as a useful yet flexible specification for studying a general regression relationship with varying coefficients. The article proposes a local least squares method with a kernel weight function to estimate the smooth coefficient function. The consistency of the estimator and its asymptotic normality are established. A simple statistic for testing a parametric model versus the semiparametric smooth coefficient model is proposed. An empirical application of the proposed method is presented with an estimation of the production function of the nonmetal mineral industry in China. The empirical findings show that the intermediate production and management expense has played a vital role and is an unbalanced determinant of the labor and capital elasticities of output in production.

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Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 20 (2002)
Issue (Month): 3 (July)
Pages: 412-22

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Handle: RePEc:bes:jnlbes:v:20:y:2002:i:3:p:412-22
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