Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development
研究了当纵向数据时间间隔不规则时，现有动态面板数据模型估计量失效的问题，提出了两种新估计量，并以儿童早期发展数据为例展示其应用价值。
With the increased availability of longitudinal data, dynamic panel data models have become commonplace. Moreover, the properties of various estimators of such models are well known. However, we show that these estimators break down when the data are irregularly spaced along the time dimension. Unfortunately, this is an increasingly frequent occurrence as many longitudinal surveys are collected at non-uniform intervals and no solution is currently available when time-varying covariates are included in the model. In this paper, we propose two new estimators for dynamic panel data models when data are irregularly spaced and compare their finite-sample performance to the näive application of existing estimators. We illustrate the practical importance of this issue in an application concerning early childhood development. Copyright © 2016 John Wiley & Sons, Ltd.