Sensitivity
analysis of the Jarque-Bera

and Lilliefors normality tests in

linear regression models

Marcelo Rodrigo Portela FERREIRA[1]

§ ABSTRACT: One of the assumptions
regarding the linear regression model is that errors are assumed to follow normal
distribution. This is not always necessary; however, most of the times it is
reasonable to consider it because the errors normality assumption becomes
important when we want to construct confidence intervals and conduct hypotheses
tests. There are some tests that are used to assess if one given univariate
sample follows normal distribution or not. In this article, we will evaluate,
through Monte Carlo Simulation, the performance under diverse scenarios of two
tests that are probably the most widely used: the Jarque-Bera test and the
Lilliefors test.

§ KEYWORDS: Normality; Jarque-Bera
test; Lilliefors test; Monte Carlo simulation.

[1] Departamento de Estatística, Centro de Ciências
Exatas e da Natureza, Universidade Federal de Pernambuco -–UFPE, CEP:
50740-540, Recife, PE, Brasil, E-mail: *marcelorpf@gmail.com*