predict.WARp

a method of the WARp class which produces a one-step ahead prediction by WAR(p) models

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

predict.WARp function

<dl><dt>object</dt>
<dd>A WARp object, the output of <code>WARp()</code>.</dd>
<dt>dSup</dt>
<dd>Optional, a numeric vector, the grid over which forecasted cdf/pdf is evaluated. Should be supplied/ignored with <code>expSup</code> together.</dd>
<dt>expSup</dt>
<dd>Optional, a numeric vector, the grid over the Exponential map is applied, <code>dSup</code> should cover and be denser than <code>expSup</code>. Should be supplied/ignored with <code>dSup</code> together.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Prediction by WAR(p) models — predict.WARp

<dl>

<dt>object</dt>
<dd>A WARp object, the output of <code>WARp()</code>.</dd>


<dt>dSup</dt>
<dd>Optional, a numeric vector, the grid over which forecasted cdf/pdf is evaluated. Should be supplied/ignored with <code>expSup</code> together.</dd>


<dt>expSup</dt>
<dd>Optional, a numeric vector, the grid over the Exponential map is applied, <code>dSup</code> should cover and be denser than <code>expSup</code>. Should be supplied/ignored with <code>dSup</code> together.</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd>

</dl>

predict.WARp: Prediction by WAR(p) models

Description

Usage

Value

Arguments

References

See Also