Modeling Earnings Measurement Error: A Multiple Imputation Approach
利用PSID验证研究数据，建模收入测量误差的非经典结构，并用多重插补方法估计一致的收入方程，强调在衰退期修正测量误差的重要性。
Recent survey validation studies suggest that measurement error in earnings data is pervasive and violates classical measurement error assumptions, and therefore may bias estimation of cross-section and longitudinal earnings models.We model the structure of earnings measurement error using data from the Panel Study of Income Dynamics Validation Study (PSIDVS).We then use Rubin's (1987) multiple imputation techniques to estimate consistent earnings equations under nonclassical earnings measurement error in the PSID.Our technique is readily generalized, and the empirical results demonstrate the potential importance of correcting for measurement error in earnings and related data, particularly during recessions.