NW_lrv

Given a vector of residuals, it generates the Heteroskedastic Long run variance.

The encompassing test is developed based on multi-step-ahead predictions of two nested models as in Pitarakis, J. (2023) <doi:10.48550/arXiv.2312.16099>. The statistics are standardised to a normal distribution, and the null hypothesis is that the larger model contains no additional useful information. P-values will be provided in the output.

Rong Peng

EncompassTest

Direct Multi-Step Forecast Based Comparison of Nested Models via
an Encompassing Test

NW_lrv function

<dl><dt>u</dt>
<dd>a vector of residual series, for which we recommend to use the recursive residuals from larger model.</dd>
<dt>nlag</dt>
<dd>Non-negative integer containing the lag length to use. If empty or not included,
nleg = min(floor(1.2*T^(1/3)),T) will be used.</dd>
<dt>demean</dt>
<dd>Logical true of false (0 or 1) indicating whether the mean should be subtracted when computing.</dd></dl>

Arguments

Long-run covariance estimation using Newey-West (Bartlett) weights — NW_lrv

<dl>

<dt>u</dt>
<dd>a vector of residual series, for which we recommend to use the recursive residuals from larger model.</dd>


<dt>nlag</dt>
<dd>Non-negative integer containing the lag length to use. If empty or not included,
nleg = min(floor(1.2*T^(1/3)),T) will be used.</dd>


<dt>demean</dt>
<dd>Logical true of false (0 or 1) indicating whether the mean should be subtracted when computing.</dd>

</dl>

NW_lrv: Long-run covariance estimation using Newey-West (Bartlett) weights

Description

Usage

Value

Arguments

Examples