Institute of Electronic Technology, the PLA Information Engineering University

This paper is concerned with the estimation
of varying-coefficient model that is frequently used in statistical
modeling. The wavelet procedures are developed to estimate the
coefficient functions. The advantage of this approach is to avoid
the restrictive smoothness requirement for nonparametric function of
the traditional smoothing approaches for varying-coefficient model,
such as kernel and local polynomial methods. Furthermore, the
convergence rate of the wavelet estimators is derived and the
asymptotic normality is established. Finite sample properties are
studied through Monte Carlo simulations.