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Estimation in Semiparametric Time Series Regression


  • Jia Chen

    (School of Economics, University of Adelaide)

  • Jiti Gao

    () (School of Economics, University of Adelaide)

  • Degui Li

    (School of Economics, University of Adelaide)


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 asymptotic theory is established in each case. An empirical application is also included.

Suggested Citation

  • Jia Chen & Jiti Gao & Degui Li, 2010. "Estimation in Semiparametric Time Series Regression," School of Economics Working Papers 2010-27, University of Adelaide, School of Economics.
  • Handle: RePEc:adl:wpaper:2010-27

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    References listed on IDEAS

    1. Le Van, C. & Page, F.H.Jr. & Wooders, M., 2001. "Arbitrage and Equilibrium in Economies with Externalities," The Warwick Economics Research Paper Series (TWERPS) 588, University of Warwick, Department of Economics.
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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. 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.
    3. 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.
    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.

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