<p>The time series of output gap (x), inflation (pi) and interest rate (r) are taken from the FRED database and transformed as in Herwartz &amp; Ploedt (2016). The trivariate time series model is commonly used to analyze monetary policy shocks.
  <br>
  Quarterly observations from 1965Q1 to 2008Q3:</p><table class="table">
<tr><td>x</td><td>Percentage log-deviation of real GDP wrt the estimate of potential output by the Congressional Budget Office</td></tr>
<tr><td>pi</td><td>Annualized quarter-on-quarter growth of the GDP deflator</td></tr>
<tr><td>i</td><td>Interest rate on Federal funds</td></tr>
</table>
<p>A more detailed description of the data and a corresponding VAR model implementation can be found in Herwartz &amp; Ploedt (2016).</p>

Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>.
Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural
impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>),  patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>),
independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>),
smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).

Alexander Lange

svars

Data-Driven Identification of SVAR Models

Bernhard Dalheimer

Helmut Herwartz

Simone Maxand

Hannes Riebl

USA function

<p>A <code>data.frame</code> containing 174 observations on 3 variables.</p>

Format

<p>The time series of output gap (x), inflation (pi) and interest rate (r) are taken from the FRED database and transformed as in Herwartz &amp; Ploedt (2016). The trivariate time series model is commonly used to analyze monetary policy shocks.
  <br />
  Quarterly observations from 1965Q1 to 2008Q3:</p><table class='table'>
<tr><td>x</td><td>Percentage log-deviation of real GDP wrt the estimate of potential output by the Congressional Budget Office</td></tr>
<tr><td>pi</td><td>Annualized quarter-on-quarter growth of the GDP deflator</td></tr>
<tr><td>i</td><td>Interest rate on Federal funds</td></tr>
</table>
<p>A more detailed description of the data and a corresponding VAR model implementation can be found in Herwartz &amp; Ploedt (2016).</p>

USA: US macroeconomic time series

Description

Usage

Arguments

Format

x	Percentage log-deviation of real GDP wrt the estimate of potential output by the Congressional Budget Office
pi	Annualized quarter-on-quarter growth of the GDP deflator
i	Interest rate on Federal funds