The partially-matched-sample correction in pseudo panel minimum distance estimation
针对重复截面数据中部分匹配样本的问题，提出对伪面板模型最小距离估计的最优权重矩阵进行校正，以提高估计效率并确保推断正确性，并以CPS数据估计教育回报为例说明。
Summary Certain repeated cross-sectional data sets, such as the Current Population Survey (CPS), use special sampling designs by which samples from different times periods are partially matched. This paper proposes a correction to the optimal weighting matrix in minimum distance (MD) estimation of pseudo-panel models to account for such partially matched samples. This partially matched sample correction may be needed if the sample matching rate is nontrivial and, at the same time, there is a fixed effect, a serially correlated idiosyncratic error, or both in the underlying linear panel data model data generating process, all of which lead to a block diagonal structure of the optimal weighting matrix. Using the correction can result in considerable efficiency gains both in finite sample and asymptotically. Furthermore, it is shown that this correction is needed, not only for the optimal MD estimator, but for any MD estimator to make the inference right. As an illustration, the correction is applied to the classical question of estimating the monetary return to education using the yearly Merged Outgoing Rotation Group files from CPS.