It is shown that least absolute deviation (LAD) autoregressive (AR) estimates converge to the parameters of an nth-order finite-impulse-response (FIR) filter which minimizes the expectation of the absolute value of the prediction error. Examples are presented in which these parameters are calculated and the efficiencies of the LAD estimates are determined from a Monte Carlo simulation. Applications to order selection and PARCOR parameter estimation are discussed.<>