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Estimation and model specification testing in nonparametric and semiparametric econometric models

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Author Info
Gao, Jiti
King, Maxwell

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Abstract

This paper considers two classes of semiparametric nonlinear regression models, in which nonlinear components are introduced to reflect the nonlinear fluctuation in the mean. A general estimation and testing procedure for nonparametric time series regression under the strong-mixing condition is introduced. Several test statistics for testing nonparametric significance, linearity and additivity in nonparametric and semi-parametric time series econometric models are then constructed. The proposed test statistics are shown to have asymptotic normal distributions under their respective null hypotheses. Moreover, the proposed testing procedures are illustrated by several simulated examples. In addition, one of the proposed testing procedures is applied to a continuous-time model and implemented through a set of the US Federal interest rate data. Our research suggests that it is unreasonable to assume the linearity in the drift for the given data as required by some existing studies.

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File URL: http://mpra.ub.uni-muenchen.de/11989/
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Publisher Info
Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 11989.

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Date of creation: Mar 2003
Date of revision: Feb 2006
Handle: RePEc:pra:mprapa:11989

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Related research
Keywords: Estimation; model specification; semi-parametric error correction model; stochastic process;

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Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

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