2002/05/15 by Timothy B. Erickson, Toni M. Whited · 2 citations
Economics, Econometrics and Finance · Decision Sciences · #Monetary Policy and Economic Impact #Spatial and Panel Data Analysis #Efficiency Analysis Using DEA
paper · doi:10.1017/s0266466602183101
openalex publication_date 2002/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
We consider a multiple mismeasured regressor errors-in-variables model where the measurement and equation errors are independent and have moments of every order but otherwise are arbitrarily distributed. We present parsimonious two-step generalized method of moments (GMM) estimators that exploit overidentifying information contained in the high-order moments of residuals obtained by “partialling out” perfectly measured regressors. Using high-order moments requires that the GMM covariance matrices be adjusted to account for the use of estimated residuals instead of true residuals defined by population projections. This adjustment is also needed to determine the optimal GMM estimator. The estimators perform well in Monte Carlo simulations and in some cases minimize mean absolute error by using moments up to seventh order. We also determine the distributions for functions that depend on both a GMM estimate and a statistic not jointly estimated with the GMM estimate.