Bayesian Approach for MTAR Models with Missing Data
tsregime object ggplot for the outputs on the function tsregime
Estimation of a MTAR model for some data
Create a complete ggplot appropriate to a particular data type
regime_missing object ggplot for the outputs on the function outputs m...
regime_model object ggplot for the outputs on the function outputs mta...
Residual diagnosis for model MTAR
Multivariate normal density using Brobdingnag class
Wishart density using Brobdingnag class
Create indicator vector for the regimen of each observation
Object class ‘regime’ creation
Organization and check model specification
Estimation of missing values of observed, covariate and threshold proc...
Compute NAIC of a MTAR model
Estimation of non-structural parameters for MTAR model
Estimation of the number of regimes in a MTAR model
Multivariate threshold autoregressive process simulation
Estimation of structural parameters of MTAR model
print an object appropriate to a particular data type
Print estimates of a regime_missing object of the function output mtar...
print regime_model object for the function outputs mtarns and mtastr
print regime_number object for the function outputs mtarnumreg
Print tsregime object
Function to make product of elements of a list
Function to create list of matrix objects
Creation of class ‘tsregime’ for some 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) <doi:10.1080/03610926.2014.990758>.