We extend two general methods of moment estimators to panel vector
autoregression models (PVAR) with p lags of endogenous variables, predetermined
and strictly exogenous variables. This general PVAR model contains the first
difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>,
Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator
by Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>. We also
provide specification tests (Hansen overidentification test, lag selection
criterion and stability test of the PVAR polynomial) and classical structural
analysis for PVAR models such as orthogonal and generalized impulse response
functions, bootstrapped confidence intervals for impulse response analysis and
forecast error variance decompositions.

Panel Vector Autoregression

package-ubuntu-20.04-r-4.0.5#cran#panelvar-0.5.3

<p>Orthogonal Impulse Response Function
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>model</code></td>
<td>
<p>A PVAR model</p>
</td>
</tr>
<tr valign="top">
<td><code>n.ahead</code></td>
<td>
<p>Any stable AR() model has an infinite MA representation. Hence any shock can be simulated infinitely into the future. For each forecast step t you need an addtional MA term.</p>
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#panelvar-0.5.3#docs#oirf

oirf

Description

Usage

Arguments

panelvar

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`model`	A PVAR model
`n.ahead`	Any stable AR() model has an infinite MA representation. Hence any shock can be simulated infinitely into the future. For each forecast step t you need an addtional MA term.