Smith for helpful comments and to Richard Blundell for kindly making his data available. In 1imi ted dependent variable models the estimators obtained by maximising the normal likelihood function are generally inconsistent when the assumption of normality ls falseo Arabmazar and Schmidt (1982) have shown that the bias trom non-normality can be substantial when the degree of censoring (or trunca...

The asymptotic properties of the quasi-maximum likelihood estimator (QMLE) of vector autoregressive moving-average (VARMA) models are derived under the assumption that the errors are uncorrelated but not necessarily independent. Relaxing the independence assumption considerably extends the range of application of the VARMA models, and allows to cover linear representations of general nonlinear ...

Normality is a common assumption for many quality control charts. One should expect misleading results once this assumption is violated. In order to avoid this pitfall, we need to evaluate this assumption prior to the use of control charts which require normality assumption. However, in certain cases either this assumption is overlooked or it is hard to check. Robust control charts and bootstra...

This paper discusses the normality assumption of the market model errors, conventionally accepted. Some other possible specifications are proposed and their performance is testing using a test statistic based on the empirical distribution function of the residuals of the model and assuming that the null distribution can depend on some unknown parameters. The parametric bootstrap method is used....

In Multivariate regression, we need to assess normality assumption simultaneously, not univariately. Univariate normal distribution does guarantee the occurrence of multivariate [1]. So extend assessment univariate into methods. One extended method is skewness and kurtosis as proposed by Mardia [2]. this paper, introduce method, present procedure show how examine in regression study case using ...

Assessing the assumption of multivariate normality is required by many parametric multivariate statistical methods, such as discriminant analysis, principal component analysis, MANOVA, etc. Here, we present an R package to asses multivariate normality. The MVN package contains three most widely used multivariate normality tests, including Mardia’s, Henze-Zirkler’s and Royston’s multivariate nor...

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your perso...