Semiparametric trending panel data models with cross-sectional dependence

Jia Chen, Jiti Gao, Degui Li

Research output: Contribution to journalArticleResearchpeer-review

Abstract

A semiparametric fixed effects model is introduced to describe the nonlinear trending phenomenon in panel data analysis and it allows for the cross-sectional dependence in both the regressors and the residuals. A pooled semiparametric profile likelihood dummy variable approach based on the first-stage local linear fitting is developed to estimate both the parameter vector and the nonlinear time trend function. As both the time series length T and the cross-sectional size N tend to infinity, the resulting estimator of the parameter vector is asymptotically normal with a root-(NT) convergence rate. Meanwhile, the asymptotic distribution for the nonparametric estimator of the trend function is also established with a root-(NTh) convergence rate. Two simulated examples are provided to illustrate the finite sample performance of the proposed method. In addition, the proposed model and estimation method are applied to a CPI data set as well as an inputa??output data set.
Original languageEnglish
Pages (from-to)71 - 85
Number of pages15
JournalJournal of Econometrics
Volume171
Issue number1
DOIs
Publication statusPublished - 2012

Cite this

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title = "Semiparametric trending panel data models with cross-sectional dependence",
abstract = "A semiparametric fixed effects model is introduced to describe the nonlinear trending phenomenon in panel data analysis and it allows for the cross-sectional dependence in both the regressors and the residuals. A pooled semiparametric profile likelihood dummy variable approach based on the first-stage local linear fitting is developed to estimate both the parameter vector and the nonlinear time trend function. As both the time series length T and the cross-sectional size N tend to infinity, the resulting estimator of the parameter vector is asymptotically normal with a root-(NT) convergence rate. Meanwhile, the asymptotic distribution for the nonparametric estimator of the trend function is also established with a root-(NTh) convergence rate. Two simulated examples are provided to illustrate the finite sample performance of the proposed method. In addition, the proposed model and estimation method are applied to a CPI data set as well as an inputa??output data set.",
author = "Jia Chen and Jiti Gao and Degui Li",
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Semiparametric trending panel data models with cross-sectional dependence. / Chen, Jia; Gao, Jiti; Li, Degui.

In: Journal of Econometrics, Vol. 171, No. 1, 2012, p. 71 - 85.

Research output: Contribution to journalArticleResearchpeer-review

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AU - Chen, Jia

AU - Gao, Jiti

AU - Li, Degui

PY - 2012

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AB - A semiparametric fixed effects model is introduced to describe the nonlinear trending phenomenon in panel data analysis and it allows for the cross-sectional dependence in both the regressors and the residuals. A pooled semiparametric profile likelihood dummy variable approach based on the first-stage local linear fitting is developed to estimate both the parameter vector and the nonlinear time trend function. As both the time series length T and the cross-sectional size N tend to infinity, the resulting estimator of the parameter vector is asymptotically normal with a root-(NT) convergence rate. Meanwhile, the asymptotic distribution for the nonparametric estimator of the trend function is also established with a root-(NTh) convergence rate. Two simulated examples are provided to illustrate the finite sample performance of the proposed method. In addition, the proposed model and estimation method are applied to a CPI data set as well as an inputa??output data set.

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