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Semiparametric Trending Panel Data Models with Cross-Sectional Dependence

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Author Info

  • Jia Chen

    (School of Economics, University of Adelaide)

  • Jiti Gao

    ()
    (School of Economics, University of Adelaide)

  • Degui Li

    (School of Economics, University of Adelaide)

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 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.

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File URL: http://www.economics.adelaide.edu.au/research/papers/doc/wp2010-10.pdf
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Bibliographic Info

Paper provided by University of Adelaide, School of Economics in its series School of Economics Working Papers with number 2010-10.

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Length: 30 pages
Date of creation: May 2010
Date of revision:
Handle: RePEc:adl:wpaper:2010-10

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Postal: Adelaide SA 5005
Phone: (618) 8303 5540
Web page: http://www.economics.adelaide.edu.au/
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Keywords: cross-sectional dependence; nonlinear time trend; panel data; profile likelihood; semiparametric regression;

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References

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  1. Peter C.B. Phillips, 2000. "Trending Time Series and Macroeconomic Activity: Some Present and Future Challenges," Cowles Foundation Discussion Papers 1264, Cowles Foundation for Research in Economics, Yale University.
  2. 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.
  3. Robinson, Peter M., 2012. "Nonparametric trending regression with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 169(1), pages 4-14.
  4. Jinhong You & Xian Zhou & Yong Zhou, 2011. "Series Estimation in Partially Linear In‐Slide Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(1), pages 89-107, 03.
  5. Peter C. B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Econometrica, Econometric Society, vol. 67(5), pages 1057-1112, September.
  6. Duffy, John & Papageorgiou, Chris, 2000. " A Cross-Country Empirical Investigation of the Aggregate Production Function Specification," Journal of Economic Growth, Springer, vol. 5(1), pages 87-120, March.
  7. Dale J. Poirier, 1995. "Intermediate Statistics and Econometrics: A Comparative Approach," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262161494, December.
  8. Peter C. B. Phillips, 2010. "The Mysteries of Trend," Cowles Foundation Discussion Papers 1771, Cowles Foundation for Research in Economics, Yale University.
  9. Degui Li & Jia Chen & Jiti Gao, 2010. "Nonparametric Time-Varying Coefficient Panel Data Models with Fixed Effects," School of Economics Working Papers 2010-08, University of Adelaide, School of Economics.
  10. 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.
  11. Hsiao,Cheng, 2003. "Analysis of Panel Data," Cambridge Books, Cambridge University Press, number 9780521818551, April.
  12. 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.
  13. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
  14. 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.
  15. Cai, Zongwu, 2007. "Trending time-varying coefficient time series models with serially correlated errors," Journal of Econometrics, Elsevier, vol. 136(1), pages 163-188, January.
  16. Chen, Jia & Gao, Jiti & Li, Degui, 2012. "A New Diagnostic Test For Cross-Section Uncorrelatedness In Nonparametric Panel Data Models," Econometric Theory, Cambridge University Press, vol. 28(05), pages 1144-1163, October.
  17. 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.
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Citations

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Cited by:
  1. Bin Peng & Chaohua Dong & Jiti Gao, 2014. "Semiparametric Single-Index Panel Data Models with Cross-Sectional Dependence," Monash Econometrics and Business Statistics Working Papers 9/14, Monash University, Department of Econometrics and Business Statistics.
  2. Yonghui Zhang & Liangjun Su & Peter C.B. Phillips, 2011. "Testing for Common Trends in Semiparametric Panel Data Models with Fixed Effects," Cowles Foundation Discussion Papers 1832, Cowles Foundation for Research in Economics, Yale University.
  3. Lena Korber & Oliver Linton & Michael Vogt, 2013. "A semiparametric model for heterogeneous panel data with fixed effects," CeMMAP working papers CWP02/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Jia Chen & Degui Li & Jiti Gao, 2013. "Non- and Semi-Parametric Panel Data Models: A Selective Review," Monash Econometrics and Business Statistics Working Papers 18/13, Monash University, Department of Econometrics and Business Statistics.
  5. Lee, Yoon-Jin, 2014. "Testing a linear dynamic panel data model against nonlinear alternatives," Journal of Econometrics, Elsevier, vol. 178(P1), pages 146-166.
  6. KiHoon Jimmy Hong & Bin Peng & Xiaohui Zhang, 2014. "Capturing the Impact of Latent Industry-Wide Shocks with Dynamic Panel Model," Research Paper Series 347, Quantitative Finance Research Centre, University of Technology, Sydney.

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