Handle: RePEc:boc:bocode:s456701 Note: This module should be installed from within Stata … Skewness – Skewness measures the degree and direction of asymmetry. The following code shows how to perform this test: jarque.test(data) Jarque-Bera Normality Test data: data JB = 5.7097, p-value = 0.05756 alternative hypothesis: greater The p-value of the test turns out to be 0.05756. If returns are stored in a row. The question arises in statistical analysis of deciding how skewed a distribution can be before it is considered a problem. Check histogram of residuals using the following stata command . Figure 1: Returns are stored in a row. gra res, normal bin(50) /* normal option superimposes a normal distribution on the graph */ Residuals show signs of right skewness (residuals bunched to left – not symmetric) and kurtosis (leptokurtic – since peak of distribution higher than expected for a normal distribution) Their histogram is shown below. Skewness. This post uses the formula that yields the same skewness as the Stata command sum var, detail reports. The first thing you usually notice about a distribution’s shape is whether it has one mode (peak) or more than one. Figure 2: Returns are stored in a column. In a well-known article, Delong and Summers (1985) studied whether business cycles are symmetrical by applying the skewness coefﬁcient to GDP, industrial production, and the unemployment rate. "MVSKTEST: Stata module to test for multivariate skewness and kurtosis," Statistical Software Components S456701, Boston College Department of Economics. of 0.0010 for skewness, 0.0442 for kurtosis, and 0.0024 for the joint univariate test. Determining if skewness and kurtosis are significantly non-normal. However, because the sampling distribu-tion of the skewness coefﬁcient for serially correlated data is Stata does not provide a command to calculate the skewness in this situation. Joint test for Normality on e: chi2(2) = 18.29 Prob > chi2 = 0.0001 Joint test for Normality on u: chi2(2) = 1.36 Prob > chi2 = 0.5055 model 2 Tests for skewness and kurtosis Number of obs = 370 Replications = 50 (Replications based on 37 clusters in CUID) The following Stata commands will do the job. Others use simple statistics to test skew-ness. It can get rid of skewness Before log-transformation After log-transformation 0 2 4 6 8 10 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35-2.5 0.0 2.5 2. The univariate tests of the other three variables do not lead to a rejection of the null hypothesis of normality. Taking logs of the data is often useful for data that are >0 because: 1. Therefore, right skewness is positive skewness which means skewness > 0. This first example has skewness = 2.0 as indicated in the right top corner of the graph. The scores are strongly positively skewed. Another variable -the scores on test 2- turn out to have skewness = -1.0. Negative (Left) Skewness Example. 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