On the Maximum Likelihood estimation of a linear structural relationship when the intercept is known

This paper considers the Maximum Likelihood (ML) estimation of the five parameters of a linear structural relationship y = [alpha] + [beta]x when [alpha] is known. The parameters are [beta], the two variances of observation errors on x and y, the mean and variance of x. When the ML estimates of the parameters cannot be obtained by solving a simple simultaneous system of five equations, they are found by maximizing the likelihood function directly. Some asymptotic properties of the estimates are also obtained.