mat_power

<code>mat_power</code> computes the j:th power of a square matrix A using
exponentiation by squaring.

internal

Penalized and non-penalized maximum likelihood estimation of smooth
transition vector autoregressive models with various types of transition weight
functions, conditional distributions, and identification methods. Constrained
estimation with various types of constraints is available. Residual based
model diagnostics, forecasting, simulations, and calculation of impulse
response functions, generalized impulse response functions, and generalized
forecast error variance decompositions. See
Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>,
Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>,
Markku Lanne, Savi Virolainen (2025) <doi:10.48550/arXiv.2403.14216>,
Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.

Savi Virolainen

sstvars

Toolkit for Reduced Form and Structural Smooth Transition Vector
Autoregressive Models

mat_power function

<dl><dt>A</dt>
<dd>A square numeric matrix.</dd>
<dt>j</dt>
<dd>A natural number representing the power to which the matrix will be raised.</dd></dl>

Arguments

Compute the j:th power of a square matrix A — mat_power

<dl>

<dt>A</dt>
<dd>A square numeric matrix.</dd>


<dt>j</dt>
<dd>A natural number representing the power to which the matrix will be raised.</dd>

</dl>

mat_power: Compute the j:th power of a square matrix A

Description

Usage

Value

Arguments