summary.sparseDFM

print.sparseDFM

Summary and print outputs for class 'sparseDFM'.

Implementation of various estimation methods for dynamic factor models (DFMs) including principal components analysis (PCA) Stock and Watson (2002) <doi:10.1198/016214502388618960>, 2Stage Giannone et al. (2008) <doi:10.1016/j.jmoneco.2008.05.010>, expectation-maximisation (EM) Banbura and Modugno (2014) <doi:10.1002/jae.2306>, and the novel EM-sparse approach for sparse DFMs Mosley et al. (2023) <arXiv:2303.11892>. Options to use classic multivariate Kalman filter and smoother (KFS) equations from Shumway and Stoffer (1982) <doi:10.1111/j.1467-9892.1982.tb00349.x> or fast univariate KFS equations from Koopman and Durbin (2000) <doi:10.1111/1467-9892.00186>, and options for independent and identically distributed (IID) white noise or auto-regressive (AR(1)) idiosyncratic errors. Algorithms coded in 'C++' and linked to R via 'RcppArmadillo'.

Alex Gibberd

sparseDFM

Estimate Dynamic Factor Models with Sparse Loadings

Luke Mosley

Tak-Shing Chan

summary.sparseDFM function

<dl><dt>x</dt>
<dd>an object of class 'sparseDFM'</dd>
<dt>...</dt>
<dd>Further <code>summary</code> arguments.</dd>
<dt>object</dt>
<dd>an object of class 'sparseDFM'</dd></dl>

Arguments

sparseDFM Summary Outputs — summary.sparseDFM

<dl>

<dt>x</dt>
<dd>an object of class 'sparseDFM'</dd>


<dt>...</dt>
<dd>Further <code>summary</code> arguments.</dd>


<dt>object</dt>
<dd>an object of class 'sparseDFM'</dd>

</dl>

summary.sparseDFM: sparseDFM Summary Outputs

Description

Usage

Value

Arguments