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

Tania Roa, Soledad Torres, Ciprian Tudor

Research output: Contribution to journalArticlepeer-review

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 (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.

Original languageEnglish
JournalCommunications in Statistics - Theory and Methods
DOIs
StateAccepted/In press - 2021
Externally publishedYes

Keywords

  • Least squares estimator
  • asymptotic normality
  • random times
  • regression model

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