Wavelet Estimation in Varying-Coefficient Models

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.