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Abstract
This article presents robust J and encompassing tests for testing nonnested hypotheses in the presence of outliers in the data. The proposed tests are based on least absolute deviations (LAD) and M-estimators unlike the conventional J and encompassing tests, which are based on least squares or maximum likelihood estimators. These tests can lead to more reliable inference in the presence of outliers than tests based on nonrobust estimators. The tests are illustrated with applications to two economic data sets and an artificially generated data set and compared with their nonrobust counterparts.