Measurement Error in Earnings Data: Using a Mixture Model Approach to Combine Survey and Register Data
研究了如何结合调查数据和行政数据来改进收入测量，发现行政数据在存在少量不匹配时表现不佳，而结合两者的预测方法更优。
Survey data on earnings tend to contain measurement error. Administrative data are superior in principle, but they are worthless in case of a mismatch. We develop methods for prediction in mixture factor analysis models that combine both data sources to arrive at a single earnings figure. We apply the methods to a Swedish data set. Our results show that register earnings data perform poorly if there is a (small) probability of a mismatch. Survey earnings data are more reliable, despite their measurement error. Predictors that combine both and take conditional class probabilities into account outperform all other predictors.