oirf

Orthogonal Impulse Response Function

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.

Robert Ferstl

panelvar

Panel Vector Autoregression

Michael Sigmund

oirf function

<dl><dt>model</dt>
<dd>A PVAR model</dd>
<dt>n.ahead</dt>
<dd>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.</dd></dl>

Arguments

Orthogonal Impulse Response Function — oirf

<dl>

<dt>model</dt>
<dd>A PVAR model</dd>


<dt>n.ahead</dt>
<dd>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.</dd>

</dl>

oirf: Orthogonal Impulse Response Function

Description

Usage

Arguments

Examples