`jarque.bera.test(x, fc = 3.5, ...)`

x

a time series of residuals or an object of class

`Arima`

.fc

a numeric. Factor to asses whether the first residual observations
are to be omitted. Ignored if

`x`

is not an `Arima`

object. See details....

further arguments. Currently omitted.

- A list containing one
`htest`

object for the null hypothesis that the kurtosis is $3$, the skewness is $0$ and a test combining both the kurtosis and the skewness to test for the normality of the input data.

`jarque.bera.test`

available in package The input can be a time series of residuals, `jarque.bera.test.default`

,
or an `Arima`

object, `jarque.bera.test.Arima`

from which the residuals
are extracted.
In the former case the whole input series of residuals is used.
In the latter case,
the first $n0$ (defined below) residuals are omitted if they are are equal to zero
or if any of them are in absolute value larger than `fc`

times
the standard deviation of the remaining residuals.
$n0$ is set equal to `x$arma[6] + x$arma[5] * x$arma[7]`

, i.e.
the number of regular differences times the periodicity of the data times
the number of seasonal differences. If $n0$ happens to be equal to $1$
it is set to $2$.

If the latter trimming operation is not desired,
the argument `fc`

can be set to a high value to ensure the complete
series of residuals in considered; or the function can be called
as `jarque.bera.test(residuals(x))`

.

Missing observations are omitted.

`print.mhtest`

.# fit an ARIMA model to the HICP 011600 series # ARIMA(0,1,0)(2,0,1) was chosen by forecast::auto.arima(ic = "bic") # normality of the residuals is rejected at the 5% significance level # due to an excess of kurtosis data("hicp") y <- log(hicp[["011600"]]) fit1 <- arima(y, order = c(0, 1, 0), seasonal = list(order = c(2, 0, 1))) jarque.bera.test(fit1) jarque.bera.test(residuals(fit1)) # fit ARIMA model for the same series including outliers that were # detected by "tsoutliers" and for the model chosen by "auto.arima" # normality of the residuals is not rejected at the 5% significance level # after including the presence of outliers mo <- outliers(c("AO", "AO", "LS", "LS"), c(79, 210, 85, 225)) xreg <- outliers.effects(mo, length(y)) fit2 <- arima(y, order = c(1, 1, 0), seasonal = list(order = c(2, 0, 2)), xreg = xreg) jarque.bera.test(fit2)

Run the code above in your browser using DataCamp Workspace