Qt_data

Simulate data from time-varying trend model

Plug-in and difference-based long-run covariance matrix estimation for time series regression. Two applications of hypothesis testing are also provided. The first one is for testing for structural stability in coefficient functions. The second one is aimed at detecting long memory in time series regression. Lujia Bai and Weichi Wu (2024)<doi:10.3150/23-BEJ1680> Zhou Zhou and Wei Biao Wu(2010)<doi:10.1111/j.1467-9868.2010.00743.x> Jianqing Fan and Wenyang Zhang<doi:10.1214/aos/1017939139> Lujia Bai and Weichi Wu(2024)<doi:10.1093/biomet/asae013> Dimitris N. Politis, Joseph P. Romano, Michael Wolf(1999)<doi:10.1007/978-1-4612-1554-7> Weichi Wu and Zhou Zhou(2018)<doi:10.1214/17-AOS1582>.

Lujia Bai

mlrv

Long-Run Variance Estimation in Time Series Regression

Weichi Wu

Qt_data function

<dl><dt>T_n</dt>
<dd>integer, sample size</dd>
<dt>param</dt>
<dd>a list of parameters<ul>
<li>tw double, squared root of variance of the innovations</li>
<li>rate double, magnitude of non-stationarity</li>
<li>center double, the center of the ar coefficient</li>
<li>ma_rate double, ma coefficient</li>
</ul></dd></dl>

Arguments

Simulate data from time-varying trend model — Qt_data

<dl>

<dt>T_n</dt>
<dd>integer, sample size</dd>


<dt>param</dt>
<dd>a list of parameters<ul>
<li>tw double, squared root of variance of the innovations</li>
<li>rate double, magnitude of non-stationarity</li>
<li>center double, the center of the ar coefficient</li>
<li>ma_rate double, ma coefficient</li>
</ul></dd>

</dl>

Qt_data: Simulate data from time-varying trend model

Description

Usage

Value

Arguments

Examples