lrv.spc.bartlett

Estimation of the long-run variance using the Barlett window.

Companion package of Carrion-i-Silvestre & Sansó (2023):
"Generalized Extreme Value Approximation to the CUMSUMQ Test for Constant
Unconditional Variance in Heavy-Tailed Time Series". It implements the Modified
Iterative Cumulative Sum of Squares Algorithm, which is an extension of
the Iterative Cumulative Sum of Squares (ICSS) Algorithm of Inclan and Tiao (1994), and it checks for changes in the
unconditional variance of a time series controlling for the tail index of
the underlying distribution. The fourth order moment is estimated non-parametrically
to avoid the size problems when the innovations are non-Gaussian (see, Sansó et al., 2004).
Critical values and p-values are generated using a Generalized Extreme Value distribution approach.
References
Carrion-i-Silvestre J.J & Sansó A (2023) <https://www.ub.edu/irea/working_papers/2023/202309.pdf>.
Inclan C & Tiao G.C (1994) <doi:10.1080/01621459.1994.10476824>,
Sansó A & Aragó V & Carrion-i-Silvestre J.L (2004) <https://dspace.uib.es/xmlui/bitstream/handle/11201/152078/524035.pdf>.

Andreu Sansó

micss

Modified Iterative Cumulative Sum of Squares Algorithm

Josep Lluís Carrion-i-Silvestre

lrv.spc.bartlett function

<dl><dt>x</dt>
<dd>Stationary variable. A numeric vector.</dd>
<dt>kmax</dt>
<dd>Maximum lag to be used for the long-run estimation of the variance.</dd></dl>

Arguments

lrv.spc.bartlett — lrv.spc.bartlett

<dl>

<dt>x</dt>
<dd>Stationary variable. A numeric vector.</dd>


<dt>kmax</dt>
<dd>Maximum lag to be used for the long-run estimation of the variance.</dd>

</dl>

lrv.spc.bartlett: lrv.spc.bartlett

Description

Usage

Value

Arguments

Details

References

Examples