Displaying 2 results from an estimated 2 matches for "heteroscedastik".
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heteroscedastic
2004 Jan 13
3
How can I test if a not independently and not identically distributed time series residuals' are uncorrelated ?
...v,1) is stationary.
Than I made Breushch-Pagan test to test if residuals are identically distributed:
library(lmtest)
bptest(merv[2:1730]~-1+merv[1:1729],~merv[1:1729]+I(merv[1:1729])^2)
BP = 81.3443, df = 2, p-value = < 2.2e-16
So merv.reg$resid aren't identically distributed. Than merv is heteroscedastik.
Finally I made Box-Ljung test to test if residuals are independently distributed:
(H0: merv.reg$resid are independently distributed)
library(ts)
merv.reg <- lm(merv[2:1730]~-1+merv[1:1729])
Box.test(merv.reg$resid, lag=25,type="Ljung")
X-squared = 54.339, df = 25, p-value = 0.00060...
2004 Jan 14
0
How can I test if a not independently and not identicallydistributed time series residuals' are uncorrelated ?
...v,1) is stationary.
Than I made Breushch-Pagan test to test if residuals are identically distributed:
library(lmtest)
bptest(merv[2:1730]~-1+merv[1:1729],~merv[1:1729]+I(merv[1:1729])^2)
BP = 81.3443, df = 2, p-value = < 2.2e-16
So merv.reg$resid aren't identically distributed. Than merv is heteroscedastik.
Finally I made Box-Ljung test to test if residuals are independently distributed:
(H0: merv.reg$resid are independently distributed)
library(ts)
merv.reg <- lm(merv[2:1730]~-1+merv[1:1729])
Box.test(merv.reg$resid, lag=25,type="Ljung")
X-squared = 54.339, df = 25, p-value = 0.00060...