Abstract

In this article, we study the limit distribution of the least square estimator, properly normalized, from a regression model in which observations are assumed to be finite (alpha N) and sampled under two different random times. Based on the limit behavior of the characteristic function and convergence result we prove the asymptotic normality for the least square estimator. We present simulations results to illustrate our theoretical results.