Uniform noise autoregressive model estimation

In this work we present a methodology for the estimation of the coefficients of an autoregressive model where the random component is assumed to be uniform white noise. The uniform noise hypothesis makes MLE become equivalent to a simple consistency requirement on the possible values of the coefficients given the data. In the case of the autoregressive model of order one, X(t+1) = aX(t) + ε(t) with i.i.d. ε(t) ~ U[a;b]; the estimator of the coefficient a can be written down analytically. The performance of the estimator is assessed via simulation.