ExpectationM_HMmdl

This function computes the log-likelihood for a Hidden Markov model and uses the Hamilton smoother to obtain smoothed probabilities of each state. This is also the expectation step in the Expectation Maximization algorithm for a Markov-switching autoregressive model.

internal

Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez Rondon & Dufour (2024) <https://grodriguezrondon.com/files/RodriguezRondon_Dufour_2024_MonteCarlo_LikelihoodRatioTest_MarkovSwitchingModels_20241015.pdf> that can be used to identify the number of regimes in Markov switching models.

Gabriel Rodriguez Rondon

MSTest

Hypothesis Testing for Markov Switching Models

Jean-Marie Dufour

ExpectationM_HMmdl function

<dl><dt>theta</dt>
<dd>Vector of model parameters.</dd>
<dt>mdl</dt>
<dd>List with model attributes.</dd>
<dt>k</dt>
<dd>Integer determining the number of regimes.</dd></dl>

Arguments

Hidden Markov model log-likelihood function — ExpectationM_HMmdl

<dl>

<dt>theta</dt>
<dd>Vector of model parameters.</dd>


<dt>mdl</dt>
<dd>List with model attributes.</dd>


<dt>k</dt>
<dd>Integer determining the number of regimes.</dd>

</dl>

ExpectationM_HMmdl: Hidden Markov model log-likelihood function

Description

Usage

Value

Arguments