Bayesian Approach for MTAR Models with Missing Data

Implements parameter estimation using a Bayesian approach for Multivariate Threshold Autoregressive (MTAR) models with missing data using Markov Chain Monte Carlo methods. Performs the simulation of MTAR processes (mtarsim()), estimation of matrix parameters and the threshold values (mtarns()), identification of the autoregressive orders using Bayesian variable selection (mtarstr()), identification of the number of regimes using Metropolised Carlin and Chib (mtarnumreg()) and estimate missing data, coefficients and covariance matrices conditional on the autoregressive orders, the threshold values and the number of regimes (mtarmissing()). Calderon and Nieto (2017) .


Reference manual

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0.1.1 by Andrey Duvan Rincon Torres, a year ago

Browse source code at

Authors: Valeria Bejarano Salcedo <[email protected]> , Sergio Alejandro Calderon Villanueva <[email protected]> Andrey Duvan Rincon Torres <[email protected]>

Documentation:   PDF Manual  

Task views: Time Series Analysis, Missing Data

GPL (>= 2) license

Imports Brobdingnag, MASS, MCMCpack, expm, ks, mvtnorm, compiler, doParallel, parallel, ggplot2

See at CRAN