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Estimation in Semiparametric Time Series Regression

Author

Listed:
  • 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

In this paper, we consider a semiparametric time series regression model and establish a set of identi cation conditions such that the model under discussion is both identi able and estimable. We then discuss how to estimate a sequence of local alternative functions nonparametrically when the null hypothesis does not hold. An asympthttps://media.adelaide.edu.au/economics/pirical application is also included.

Suggested Citation

  • Jia Chen & Jiti Gao & Degui Li, 2010. "Estimation in Semiparametric Time Series Regression," School of Economics and Public Policy Working Papers 2010-27, University of Adelaide, School of Economics and Public Policy.
    • Handle: RePEc:adl:wpaper:2010-27
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    File URL: http://www.economics.adelaide.edu.au/research/papers/doc/wp2010-27.pdf
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    Cited by:

    1. Jiti Gao, 2012. "Identification, Estimation and Specification in a Class of Semiparametic Time Series Models," Monash Econometrics and Business Statistics Working Papers 6/12, Monash University, Department of Econometrics and Business Statistics.
    2. Gao, Jiti, 2012. "Identification, Estimation and Specification in a Class of Semi-Linear Time Series Models," MPRA Paper 39256, University Library of Munich, Germany, revised 14 May 2012.
    3. Dong, Chaohua & Gao, Jiti & Tjøstheim, Dag & Yin, Jiying, 2017. "Specification testing for nonlinear multivariate cointegrating regressions," Journal of Econometrics, Elsevier, vol. 200(1), pages 104-117.
    4. George Athanasopoulos & Minfeng Deng & Gang Li & Haiyan Song, 2013. "Domestic and outbound tourism demand in Australia: a System-of-Equations Approach," Monash Econometrics and Business Statistics Working Papers 6/13, Monash University, Department of Econometrics and Business Statistics.
    5. Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.
    6. Justin Dang & Aman Ullah, 2022. "Machine-Learning-Based Semiparametric Time Series Conditional Variance: Estimation and Forecasting," JRFM, MDPI, vol. 15(1), pages 1-12, January.

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