jointnormalTest {QRMlib} | R Documentation |
provides test of multivariate normality based on analysing Mahalanobis distances
jointnormalTest(data, dist="chisquare")
data |
matrix of data with each row representing an observation |
dist |
"chisquare" performs test against chi-squared distribution, which is an approximation; "beta" performs test against a scaled beta |
see pages 69-70 of QRM
p-value for Kolmogorov-Smirnov test
a QQplot against the reference distribution is created
data(DJ); Ret.DJ <- mk.returns(DJ); selection1 <- c("AXP","EK","BA","C","KO","MSFT", "HWP","INTC","JPM","DIS"); partialDJ30dailyTS <- Ret.DJ[,selection1]; #Choose only the data from 1/1/1993 to 12/31/2000. partialDJ30daily <- window(partialDJ30dailyTS,from="1993-01-01", to="2000-12-31"); partialDJ30dailyMatrix <- seriesData(partialDJ30daily); #Note the tests on the ten stocks selected from DJ30 fail the test miserably #except possibly the quarterly values. The QQ plots are very revealing. #See p. 72 in QRM Book. jointnormalTest(partialDJ30dailyMatrix);