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Time-Varying Nonlinear Regression

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Book cover Economic Structural Change

Summary

This chapter discusses the estimation of time series models that are possibly nonlinear in parameters, which change smoothly but nonparametrically over time. We describe a time-varying, kernel-based analog of nonlinear least squares and establish consistency and asymptotic normality for the estimates, with allowance for serial dependence of a general kind in the disturbances. These results draw on general theorems for extremum estimates, which can also be applied to more general time-varying models.

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References

  • Benedetti, J.K. (1977), On the nonparametric estimation of regression functions. Journal of the Royal Statistical Society, B-39, 248–253.

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  • Robinson, P.M. (1989), Nonparametric estimation of time-varying parameters, pp. 253 – 264 in P. Hackl (ed.), Statistical Analysis and Forecasting of Economic Structural Change. Berlin: Springer-Verlag.

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Authors
  1. Peter M. Robinson
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Editor information

Editors and Affiliations

  1. Institut für Statistik, Wirtschaftsuniversität Wien, Augasse 2, A-1090, Wien, Austria

    Peter Hackl

  2. Stockholm School of Economics, Box 6501, S-113 83, Stockholm, Switzerland

    Anders Holger Westlund

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© 1991 Springer-Verlag Berlin Heidelberg

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Robinson, P.M. (1991). Time-Varying Nonlinear Regression. In: Hackl, P., Westlund, A.H. (eds) Economic Structural Change. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-06824-3_13

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  • DOI: https://doi.org/10.1007/978-3-662-06824-3_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-06826-7

  • Online ISBN: 978-3-662-06824-3

  • eBook Packages: Springer Book Archive

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