diagnostic_mtar {BMTAR}

Residual diagnosis for model MTAR

Description

Tests to help evaluate some assumptions about the MTAR model. calculating some tests and graphs.

Usage

diagnostic_mtar(regime_model,lagmax = NULL,alpha = '0.05')

Arguments

regime_model

Object of class “regime_model”. Not NULL

lagmax

maximum lag at which to calculate the acf and pacf. Default NULL

alpha

level of significance for the graphs, should take values in c('0.10','0.05','0.025','0.01','0.005'). Default '0.05'

Details

For the graphical tests it returns: “Residuals plot” and “Residuals density plot” (overlaps a standard normal density),“Residuals plot” and “Residuals plot”, “CUSUM” statistic for residuals, “ACF” and “PACF” plots for residuals series.

Value

Returns a list of ggplot objects with the graphics mentioned before.

Author(s)

Valeria Bejarano [e-mail], Sergio Calderon [e-mail] & Andrey Rincon [e-mail]

References

Calderon, S. and Nieto, F. (2017) Bayesian analysis of multivariate threshold autoregress models with missing data. Communications in Statistics - Theory and Methods 46 (1):296–318. doi:10.1080/03610926.2014.990758.

Examples

library(ggplot2)
data("datasim")
data = datasim$Sim$Z
parameters = list(l = 1,orders = list(pj = 1))
initial = mtarinipars(tsregime_obj = tsregime(data),
                      list_model = list(pars = parameters))
estim1 = mtarns(ini_obj = initial,niter = 500,chain = TRUE,burn = 500)
diagnostic_mtar(estim1)