Semiparametric Trending Panel Data Models with Cross-Sectional Dependence
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 semiparametric profile likelihood approach based on the first-stage local linear fitting is developed to estimate both the parameter vector and the time trend function.
|Date of creation:||May 2010|
|Date of revision:|
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- Degui Li & Jia Chen & Jiti Gao, 2011.
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1222, Cowles Foundation for Research in Economics, Yale University.
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"A semiparametric panel model for unbalanced data with application to climate change in the United Kingdom,"
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- Alev Atak & Oliver Linton & Zhijie Xiao, 2010. "A Semiparametric Panel Model for unbalanced data with Application to Climate Change in the United Kingdom," Boston College Working Papers in Economics 762, Boston College Department of Economics.
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1264, Cowles Foundation for Research in Economics, Yale University.
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- Jiti Gao & Kim Hawthorne, 2006. "Semiparametric estimation and testing of the trend of temperature series," Econometrics Journal, Royal Economic Society, vol. 9(2), pages 332-355, 07.
- Su, Liangjun & Ullah, Aman, 2006. "Profile likelihood estimation of partially linear panel data models with fixed effects," Economics Letters, Elsevier, vol. 92(1), pages 75-81, July.
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- Jia Chen & Jiti Gao & Degui Li, 2009. "A New Diagnostic Test for Cross-Section Independence in Nonparametric Panel Data Model," School of Economics Working Papers 2009-16, University of Adelaide, School of Economics.
- Su, Liangjun & Jin, Sainan, 2012. "Sieve estimation of panel data models with cross section dependence," Journal of Econometrics, Elsevier, vol. 169(1), pages 34-47.
- Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
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