A Semiparametric Regression Model for Longitudinal Data with Non-stationary Errors

Motivated by the need to analyze the National Longitudinal Surveys data, we propose a new semiparametric longitudinal mean-covariance model in which the effects on dependent variable of some explanatory variables are linear and others are non-linear, while the within-subject correlations are modelled by a non-stationary autoregressive error structure. We develop an estimation machinery based on least squares technique by approximating non-parametric functions via B-spline expansions and establish the asymptotic normality of parametric estimators as well as the rate of convergence for the non-parametric estimators. We further advocate a new model selection strategy in the varying-coefficient model framework, for distinguishing whether a component is significant and subsequently whether it is linear or non-linear. Besides, the proposed method can also be employed for identifying the true order of lagged terms consistently. Monte Carlo studies are conducted to examine the finite sample performance of our approach, and an application of real data is also illustrated.