New efficient spline estimation for varying-coefficient models with two-step knot number selection

Jun Jin (), Tiefeng Ma and Jiajia Dai
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Jun Jin: Southwestern University of Finance and Economics
Tiefeng Ma: Southwestern University of Finance and Economics
Jiajia Dai: Guizhou University

Metrika: International Journal for Theoretical and Applied Statistics, 2021, vol. 84, issue 5, No 4, 693-712

Abstract: Abstract One of the advantages for the varying-coefficient model is to allow the coefficients to vary as smooth functions of other variables and the coefficients functions can be estimated easily through a simple B-spline approximations method. This leads to a simple one-step estimation procedure. We show that such a one-step method cannot be optimal when some coefficient functions possess different degrees of smoothness. Under the regularity conditions, the consistency and asymptotic normality of the two step B-spline estimators are also derived. A few simulation studies show that the gain by the two-step procedure can be quite substantial. The methodology is illustrated by an AIDS data set.

Keywords: Varying-coefficient models; Asymptotic normality; B-spline; Adaptive knot selection; Two-step B-spline (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00184-020-00798-8

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