Central limit theorems for LS estimators in the EV regression model with dependent measurements

Abstract

a b s t r a c tIn this paper, we consider the simple linear errors-in-variables (EV) regression models:. . are unknown constants (parameters), (ε 1 , δ 1 ), (ε 2 , δ 2 ), . . . are errors and ξ i , η i , i = 1, 2, . . . are observable. The asymptotic normality for the least square (LS) estimators of the unknown parameters β and θ in the model are established under the assumptions that the errors are m-dependent, martingale differences, φ-mixing, ρ-mixing and α-mixing.