Abstract. The methods measuring the departure between observation and the model were reviewed. The following statistics were applied on two experimental data sets: Chi-Squared, Kolmogorov-Smirnov, Anderson-Darling, Wilks-Shapiro, and Jarque-Bera. Both investigated sets proved not to be normal

Abstract We show that the Jarque-Bera test, originally devised for constant conditional variance models with no functional dependence between conditional mean and variance parameters, can be safely applied to a broad class of GARCH-M models, but not to all. JEL Codes: C52, C15, C22

It is well known that the finite-sample null distribution of the Jarque-Bera Lagrange Multiplier (LM) test for normality and its adjusted version (ALM) introduced by Urzua differ considerably from their asymptotic χ 2 (2) limit. Here, we present results from Monte Carlo simulations using 10 7

For testing normality we investigate the power of several tests, rst of all, the well known test of Jarque and Bera (1980) and furthermore the tests of Kuiper (1960) and Shapiro and Wilk (1965) as well as tests of Kolmogorov-Smirnov and Cramer-von Mises type. The tests on normality are based, rst

The …nite-sample null distribution of the Jarque-Bera Lagrange multiplier test for normality di¤ers considerably from the asymptotic 2 (2). However, asymptotic critical values are commonly used in applied work, even for relatively small sample sizes. Here, we develop very accurate response surface

Abstract not found

We study the validity of the Jarque-Bera test for a class of univariate parametric stochastic differential equations (SDE) dXt = b(Xt, α)dt+ dZt observed at discrete time points t n i = ihn, i = 1, 2,..., n, where Z is a nondegenerate Lévy process with finite moments, and nhn → ∞ and nh2n → 0 as n

Abstract. The methods measuring the departure between observation and the model were reviewed. The following statistics were applied on two experimental data sets: ChiSquared, Kolmogorov-Smirnov, Anderson-Darling, Wilks-Shapiro, and Jarque-Bera. Both investigated sets proved not to be normal

This paper considers testing for normality for time series data. In econometrics the typical testing procedure employs the Jarque-Bera test statistic which has an asymp-totic chi-square distribution when the considered series is uncorrelated. However, with time series data it often happens

of the Jarque–Bera test.