select_order

This function fits AR(1), ... AR(9) models for all
 given time series and calculates different information
 criterions (FPE, AIC, AICC, SIC, HQ) for each of these fits.
 The result is the best fit in terms of minimizing
 the infromation criteria.

Performs a multiscale analysis of a nonparametric
regression or nonparametric regressions with time series errors. In case
of one regression, with the help of this package it is possible to detect
the regions where the trend function is increasing or decreasing.
In case of multiple regressions, the test identifies regions where
the trend functions are different from each other. See
Khismatullina and Vogt (2020) <doi:10.1111/rssb.12347>,
Khismatullina and Vogt (2022) <doi:10.48550/arXiv.2209.10841> and
Khismatullina and Vogt (2023) <doi:10.1016/j.jeconom.2021.04.010>
for more details on theory and applications.

Marina Khismatullina

MSinference

Multiscale Inference for Nonparametric Time Trend(s)

Michael Vogt

select_order function

<dl><dt>data</dt>
<dd>One or a number of time series in a matrix. Column names
of the matrix should be reasonable</dd>
<dt>q</dt>
<dd>A vector of integers that consisits of different tuning
parameters to analyse. If not supplied, q is taken to be
\([2\log{T}]:([2\sqrt{T}] + 1)\).</dd>
<dt>r</dt>
<dd>A vector of integers that consisits of different tuning
parameters r_bar for <code>estimate_lrv</code>.
If not supplied, \(r = 5, \ldots, 15\).</dd></dl>

Arguments

Calculates different information criterions for a single time series
or multiple time series with AR(\(p\)) errors
based on the long-run variance estimator(s) for a range of tuning
parameters and different orders \(p\). — select_order

<dl>

<dt>data</dt>
<dd>One or a number of time series in a matrix. Column names
of the matrix should be reasonable</dd>


<dt>q</dt>
<dd>A vector of integers that consisits of different tuning
parameters to analyse. If not supplied, q is taken to be
\([2\log{T}]:([2\sqrt{T}] + 1)\).</dd>


<dt>r</dt>
<dd>A vector of integers that consisits of different tuning
parameters r_bar for <code>estimate_lrv</code>.
If not supplied, \(r = 5, \ldots, 15\).</dd>

</dl>

Calculates different information criterions for a single time series
or multiple time series with AR(\(p\)) errors
based on the long-run variance estimator(s) for a range of tuning
parameters and different orders \(p\).

select_order: Calculates different information criterions for a single time series or multiple time series with AR(\(p\)) errors based on the long-run variance estimator(s) for a range of tuning parameters and different orders \(p\).

Description

Usage

Value

Arguments