Estimation and model identification of longitudinal data time-varying nonparametric models

Online Supplement: Nonparametric estimation of conditional distribu- tion functions with longitudinal data and time-varying parametric models

Time-varying Additive Models for Longitudinal Data

Additive model is an effective dimension reduction model that provides flexibility to model the relation between a response variable and key covariates. The literature is largely developed to scalar response and vector covariates. In this paper, more complex data is of interest, where both the response and covariates may be functions. A functional additive model is proposed together with a new ...

Nonparametric estimation of fixed effects panel data varying coefficient models

JEL classification: C14 C23 AMS subject classifications: 62G08 62G20 62P20 Keywords: Varying coefficient models Fixed effects Panel data Local linear regression Oracle efficient estimator Within estimator Profile least squares estimator a b s t r a c t In this paper, we consider the nonparametric estimation of a varying coefficient fixed effect panel data model. The estimator is based in a with...

Bayesian Bandwidth Estimation in Nonparametric Time-Varying Coefficient Models

Bandwidth plays an important role in determining the performance of nonparametric estimators, such as the local constant estimator. In this paper, we propose a Bayesian approach to bandwidth estimation for local constant estimators of time–varying coefficients in time series models. We establish a large sample theory for the proposed bandwidth estimator and Bayesian estimators of the unknown pa...

Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data

This paper considers nonparametric estimation in a varying coefficient model with repeated measurements (Yij, Xtj, tu), for i = l,...,n and j = l,...,nh where Xu = (XiJ0,..., Xijk) r and (Yy, XtJ, ttJ) denote the yth outcome, covariate and time design points, respectively, of the ith subject. The model considered here is Yu = XjjPitij) + £i(tu), where fi{t) = {fio(t),..., j? t(t)) T , for k ^ 0...