sr_shape

Computes the scale parameter for the GEV approximation
to the distribution of kappa_test statistic

internal

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

sr_shape function

<dl><dt>t</dt>
<dd>Sample size</dd>
<dt>alpha</dt>
<dd>Value of the tail index between 2 and 4</dd>
<dt>lmax</dt>
<dd>Maximum lag to be used for the long-run estimation of the fourth order moment of the
innovations. If not specified it is generated automatically depending on the sample size.</dd></dl>

Arguments

sr_shape — sr_shape

<dl>

<dt>t</dt>
<dd>Sample size</dd>


<dt>alpha</dt>
<dd>Value of the tail index between 2 and 4</dd>


<dt>lmax</dt>
<dd>Maximum lag to be used for the long-run estimation of the fourth order moment of the
innovations. If not specified it is generated automatically depending on the sample size.</dd>

</dl>

sr_shape: sr_shape

Description

Usage

Value

Arguments

Details

References