TY - JOUR

T1 - Limit distribution of the least square estimator with observations sampled at random times driven by standard Brownian motion

AU - Roa, Tania

AU - Torres, Soledad

AU - Tudor, Ciprian

N1 - Publisher Copyright:
© 2021 Taylor & Francis Group, LLC.

PY - 2021

Y1 - 2021

N2 - 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 (αN) 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.

AB - 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 (αN) 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.

KW - Least squares estimator

KW - asymptotic normality

KW - random times

KW - regression model

UR - http://www.scopus.com/inward/record.url?scp=85117321869&partnerID=8YFLogxK

U2 - 10.1080/03610926.2021.1980044

DO - 10.1080/03610926.2021.1980044

M3 - Article

AN - SCOPUS:85117321869

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

SN - 0361-0926

ER -