REGRESSION METHODOLOGY WITH GROSS OBSERVATION ERRORS IN THE EXPLANATORY VARIABLES***

The robustness of linear programming regression estimators is examined where the disturbance terms are normally distributed and there are observation errors in the explanatory variables. These errors are occasional gross biases between one set of observations and another. The simulation of short series data offers preliminary evidence that when these biases have a non-zero mean, MSAE estimation is more robust than least squares.