Estimation and testing in least absolute value regression with serially correlated disturbances

Least absolute value (LAV) regression provides a robust alternative to least squares, particularly when the disturbances follow distributions that are nonnormal and subject to outliers. While inference in least squares estimation is well-understood, inferential procedures in the context of LAV estimation have not been studied as extensively, particularly in the presence of non-independent disturbances. In this work, we study three alternative significance test procedures in LAV regression, along with two approaches used to correct for serial correlation. The study is based on large-scale Monte Carlo simulations, and comparisons are made based on both observed significance levels and power.