ainv

apinv

Matrix inverse and pseudo-inverse by the Armadillo C++ library.

Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm
or Two-Step (2S) estimation, supporting datasets with missing data. The estimation options follow advances in the
econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA -
2S estimation as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - or via iterated
Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012)
<doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>,
allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and
the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports
interpretation and visualization of the model as well as forecasting. Information criteria to choose the number
of factors are also provided - following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.

Sebastian Krantz

dfms

Dynamic Factor Models

Rytis Bagdziunas

ainv function

<dl><dt>x</dt>
<dd>a numeric matrix, must be square for <code>ainv</code>.</dd></dl>

Arguments

Armadillo's Inverse Functions — ainv

<dl>

<dt>x</dt>
<dd>a numeric matrix, must be square for <code>ainv</code>.</dd>

</dl>

ainv: Armadillo's Inverse Functions

Description

Usage

Value

Arguments

Examples