fillNA

Internal missing data is filled in using a cubic spline.
Start and end of sample missing data is filled in using the median of the series and then
smoothed with an MA(3) process.

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

fillNA function

<dl><dt>X</dt>
<dd>n x p numeric matrix of stationary and standardized time series</dd></dl>

Arguments

Interpolation of missing data — fillNA

<dl>

<dt>X</dt>
<dd>n x p numeric matrix of stationary and standardized time series</dd>

</dl>

fillNA: Interpolation of missing data

Description

Usage

Value

Arguments