Modeling Nonignorable Nonresponse in Categorical Panel Data With an Example in Estimating Gross Labor-Force Flows
针对面板调查中因非随机缺失导致的不可忽略无应答问题，提出适用于分类数据的模型，将无应答概率设为缺失变量的函数，并用最大似然估计拟合，以美国和加拿大劳动力调查为例估计劳动力流动总量。
Abstract Many large-scale sample surveys use panel designs under which sampled individuals are interviewed several times before being dropped from the sample. The longitudinal data bases available from such surveys could be used to provide estimates of gross change over time. One problem in using these data to estimate gross change is how to handle the period-to-period nonresponse. This nonresponse is typically nonrandom and, furthermore, may be nonignorable in that it cannot be accounted for by other observed quantities in the data. Under the models proposed in this article, which are appropriate for the analysis of categorical data, the probability of nonresponse may be taken to be a function of the missing variable of interest. The proposed models are fit using maximum likelihood estimation. As an example, the method is applied to the problem of estimating gross flows in labor-force participation using data from the Current Population Survey and the Canadian Labour Force Survey. KEY WORDS: Gross flowsLongitudinal dataMaximum likelihood estimationMissing dataSample survey