Compute NAIC of a MTAR model
Compute the Non-linear Akaike information criterion (NAIC) of a “regime_model” class object.
mtarNAIC(regimemodel)
regimemodel |
object of class “ |
Estimation of thresholds was made before starting the Bayesian procedure via the Non-linear Akaike information criterion (NAIC) (Tong, 1990), in MTAR model. The NAIC for a MTAR model with l regimes is:
NAIC = {∑_{j = 1}^l AICj(r)}/{∑_{j = 1}^l N_j}
AICj(r) = Nj ln(|Sj/Nj|) + 2k η_j
Nj: number of observations in each regime.
S_{j}=∑_{t:j_t = j}(y_t-YP)'(y_t-YP)
YP = Φ_{0}^(j)+∑_{i=1}^{p_j}Φ_{i}^{(j)} Y_{t-i}+∑_{i=1}^{q_j} β_{i}^{(j)} X_{t-i} + ∑_{i=1}^{d_j} δ_{i}^{(j)} Z_{t-i}
Return a list type object:
AICj |
numeric type, AIC for each regime |
NAIC |
numeric type, NAIC value |
Valeria Bejarano vbejaranos@unal.edu.co, Sergio Calderon sacalderonv@unal.edu.co & Andrey Rincon adrincont@unal.edu.co
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.
data("datasim")
data = datasim
# Estimate number of regimes with NAIC
initial1 = mtarinipars(tsregime_obj = data$Sim,
list_model = list(pars = list(l = 2,
orders = list(pj = c(1,1),dj = c(1,1)), r = 0.2)))
estruc1 = mtarns(ini_obj = initial1,niter = 100,chain = TRUE,burn = 100)
initial2 = mtarinipars(tsregime_obj = data$Sim,
list_model = list(pars = list(l = 2,
orders = list(pj = c(1,1),dj = c(1,1)), r = 0.3)))
estruc2 = mtarns(ini_obj = initial2,niter = 100,chain = TRUE,burn = 100)
#NAIC
mtarNAIC(estruc1)
mtarNAIC(estruc2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.