Fitting Markov-Modulated Linear Regression Models
Calculating the average sojourn time in each state
Estimantion of unknown Markov-modulated linear regression model parame...
Transformation of vector with initial states I for various observation...
Transformation of the observed time vector tau. Data preparation stage...
Transformation of regressors' matrix X. Data preparation stage for sim...
Estimantion of the variance of the response Y
Preparing data for parameter estimation procedure
Simulation of the vector of responses Y. Data preparation stage for si...
A set of tools for fitting Markov-modulated linear regression, where responses Y(t) are time-additive, and model operates in the external environment, which is described as a continuous time Markov chain with finite state space. Model is proposed by Alexander Andronov (2012) <arXiv:1901.09600v1> and algorithm of parameters estimation is based on eigenvalues and eigenvectors decomposition. Markov-switching regression models have the same idea of varying the regression parameters randomly in accordance with external environment. The difference is that for Markov-modulated linear regression model the external environment is described as a continuous-time homogeneous irreducible Markov chain with known parameters while switching models consider Markov chain as unobserved and estimation procedure involves estimation of transition matrix. These models have significant differences in terms of the analytical approach. Also, package provides a set of data simulation tools for Markov-modulated linear regression (for academical/research purposes). Research project No. 1.1.1.2/VIAA/1/16/075.