emp_acf

Computes autocovariances at lags 0 to p for the ell-th differences of data.

internal

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

emp_acf function

<dl><dt>data</dt>
<dd>Time series for which we calculate autocovariances.</dd>
<dt>ell</dt>
<dd>Order of differences used.</dd>
<dt>p</dt>
<dd>Maximum lag for the autocovariances</dd></dl>

Arguments

Computes autocovariances at lags 0 to p for the ell-th differences of data. — emp_acf

<dl>

<dt>data</dt>
<dd>Time series for which we calculate autocovariances.</dd>


<dt>ell</dt>
<dd>Order of differences used.</dd>


<dt>p</dt>
<dd>Maximum lag for the autocovariances</dd>

</dl>

emp_acf: Computes autocovariances at lags 0 to p for the ell-th differences of data.

Description

Usage

Value

Arguments