make_knots

This function creates a knot vector for splines. The knots are distributed
according to a beta distribution. The first input defines the number of inner
knots. The total number of knots is <code>n + 2 * order</code>.

Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <arXiv:2102.00968> <doi:10.1016/j.jeconom.2021.11.008>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <arXiv:1404.1356>. Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization <https://github.com/kthohr/optim>.

Jonathan Berrisch

profoc

Probabilistic Forecast Combination Using CRPS Learning

Florian Ziel

make_knots function

<dl><dt>n</dt>
<dd>Number of knots</dd>
<dt>mu</dt>
<dd>Beta distribution location parameter</dd>
<dt>sig</dt>
<dd>Beta distribution scale parameter</dd>
<dt>nonc</dt>
<dd>Beta distribution noncentrality parameter</dd>
<dt>tailw</dt>
<dd>Tailweight</dd>
<dt>deg</dt>
<dd>Degree of splines</dd></dl>

Arguments

Create a vector of knots for splines — make_knots

<dl>

<dt>n</dt>
<dd>Number of knots</dd>


<dt>mu</dt>
<dd>Beta distribution location parameter</dd>


<dt>sig</dt>
<dd>Beta distribution scale parameter</dd>


<dt>nonc</dt>
<dd>Beta distribution noncentrality parameter</dd>


<dt>tailw</dt>
<dd>Tailweight</dd>


<dt>deg</dt>
<dd>Degree of splines</dd>

</dl>

make_knots: Create a vector of knots for splines

Description

Usage

Arguments