Use of ridge calibration method in predicting election results

Ridge calibration is a penalized method used in survey sampling to reduce the variability of the final set of weights by relaxing the linear restrictions. We proposed a method for selecting the penalty parameter that minimizes the estimated mean squared error of the mean estimator when estimated auxiliary information is used. We showed that the proposed estimator is asymptotically equivalent to the generalized regression estimator. A simple simulation study shows that our estimator has the smaller MSE compared to the traditional calibration ones. We applied our method to predict election result using National Barometer Survey and Korea Social Integration Survey.