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Varying coefficient partially nonlinear models with nonstationary regressors
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
School of Mathematical Sciences, Zhejiang University.
2018 (English)In: Journal of Statistical Planning and Inference, ISSN 0378-3758, E-ISSN 1873-1171, Vol. 194, p. 47-64Article in journal (Refereed) Published
Abstract [en]

We study a varying coefficient partially nonlinear model in which the regressors are generated by the multivariate unit root processes. A profile nonlinear least squares estimation procedure is applied to estimate the parameter vector and the functional coefficients. Under some mild conditions, the asymptotic distribution theory for the resulting estimators is established. The rate of convergence for the parameter vector estimator depends on the properties of the nonlinear regression function. However, the rate of convergence for the functional coefficients estimator is the same and enjoys the super-consistency property. Furthermore, a simulation study is conducted to investigate the finite sample performance of the proposed estimating procedures.

Place, publisher, year, edition, pages
2018. Vol. 194, p. 47-64
Keywords [en]
Varying coefficient partially nonlinear model Profile nonlinear least squares estimation, Local linear fitting, Unit root process
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-142949DOI: 10.1016/j.jspi.2017.10.001ISI: 000418975500005OAI: oai:DiVA.org:umu-142949DiVA, id: diva2:1165759
Funder
Swedish Research Council, 340-2013-5342Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2018-06-09Bibliographically approved

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Zhou, Zhiyong

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