residuals.sparseDFM

fitted.sparseDFM

resid.sparseDFM

Obtain the residuals or fitted values of the <code>sparseDFM</code> fit.

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

residuals.sparseDFM function

<dl><dt>object</dt>
<dd>an object of class 'sparseDFM'.</dd>
<dt>standardize</dt>
<dd>logical. The residuals and fitted values should be standardized. Default is <code>FALSE</code>, values returned in the original data \(\bm{X}\) scale.</dd>
<dt>alpha_index</dt>
<dd>Choose which L1 penalty parameter to display the results for. Default is 'best'. Otherwise, input a number between 1:length(alpha_grid) that indicates the required alpha parameter.</dd>
<dt>...</dt>
<dd>Further <code>residuals</code> arguments.</dd></dl>

Arguments

sparseDFM Residuals and Fitted Values — residuals.sparseDFM

<dl>

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


<dt>standardize</dt>
<dd>logical. The residuals and fitted values should be standardized. Default is <code>FALSE</code>, values returned in the original data \(\bm{X}\) scale.</dd>


<dt>alpha_index</dt>
<dd>Choose which L1 penalty parameter to display the results for. Default is 'best'. Otherwise, input a number between 1:length(alpha_grid) that indicates the required alpha parameter.</dd>


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

</dl>

residuals.sparseDFM: sparseDFM Residuals and Fitted Values

Description

Usage

Value

Arguments