WARp_acvfs

This function uses a time series of quantile functions to calculate the sample Wasserstein autocovariance functions from order \(0\) to \(p\) with a specified training window

internal

Implementation of the methodologies described in 1) Alexander Petersen, Xi Liu and Afshin A. Divani (2021) <doi:10.1214/20-aos1971>, including global F tests, partial F tests, intrinsic Wasserstein-infinity bands and Wasserstein density bands, and 2) Chao Zhang, Piotr Kokoszka and Alexander Petersen (2022) <doi:10.1111/jtsa.12590>, including estimation, prediction, and inference of the Wasserstein autoregressive models.

Xi Liu

Wasserstein Regression and Inference

Chao Zhang

Matthew Coleman

Alexander Petersen

WARp_acvfs function

<dl><dt>end.day</dt>
<dd>A positive integer, the last index of the training window.</dd>
<dt>training.size</dt>
<dd>A positive integer, the size of the training widnows.</dd>
<dt>quantile</dt>
<dd>A matrix containing all the available quantile functions. Columns represent time indices and rows represent evaluation grid.</dd>
<dt>quantile.grid</dt>
<dd>A numeric vector, the grid over which quantile functions are evaluated.</dd>
<dt>p</dt>
<dd>A positive integer, the maximum order of the sample Wasserstein autocovariance functions.</dd></dl>

Arguments

Function for calculating sample Wasserstein autocovariance functions — WARp_acvfs

<dl>

<dt>end.day</dt>
<dd>A positive integer, the last index of the training window.</dd>


<dt>training.size</dt>
<dd>A positive integer, the size of the training widnows.</dd>


<dt>quantile</dt>
<dd>A matrix containing all the available quantile functions. Columns represent time indices and rows represent evaluation grid.</dd>


<dt>quantile.grid</dt>
<dd>A numeric vector, the grid over which quantile functions are evaluated.</dd>


<dt>p</dt>
<dd>A positive integer, the maximum order of the sample Wasserstein autocovariance functions.</dd>

</dl>

WARp_acvfs: Function for calculating sample Wasserstein autocovariance functions

Description

Usage

Value

Arguments