summary.sarma {DCSmooth}

Summarizing SARMA/SFARIMA Estimation or Simulation

Description

summary method for class "sarma" or "sfarima"

Usage

## S3 method for class 'sarma'
summary(object, ...)

## S3 method for class 'sfarima'
summary(object, ...)

Arguments

object

an object of class "sarma" or "sfarima", usually a result of a call to the estimation functions sarma.est, sfarima.est or to the corresponding simulation functions sarma.sim and sfarima.sim.

...

Additional arguments passed to the summary.sarma/ summary.sfarima function.

Value

The function summary.sarma/summary.sfarima returns an object of class summary_sarma including

model	estimated or simulated model parameters including coefficient matrices ar, ma, the error term standard deviation sigma and the vector of long memory parameters d (summary.sarma only)
model_order	order of the estimated/simulated model computed from the matrices ar, ma.
stnry	a flag for stationarity of the short memory part.
subclass	a flag indicating whether the object inherits from an estimation (subclass = "est") or simulation procedure (subclass = "sim").

Details

summary.sarma/summary.sfarima strips an object of class "sarma"/"sfarima" from all large matrices (Y, innov), allowing for easier handling of meta-statistics of the bandwidth selection procedure.

print.summary_sarma/print.summary_sarma returns a list of summary statistics from the estimation or simulation procedure.

See Also

sarma.est, sfarima.est, sarma.sim, sfarima.sim

Examples

# SARMA Simulation and Estimation
ma = matrix(c(1, 0.2, 0.4, 0.1), nrow = 2, ncol = 2)
ar = matrix(c(1, 0.5, -0.1, 0.1), nrow = 2, ncol = 2)
sigma = 0.5
sarma_model = list(ar = ar, ma = ma, sigma = sigma)
sarma_sim = sarma.sim(100, 100, model = sarma_model)
summary(sarma_sim)
sarma_est = sarma.est(sarma_sim$Y)
summary(sarma_est)

# SFARIMA Simulation and Estimation
ma = matrix(c(1, 0.2, 0.4, 0.1), nrow = 2, ncol = 2)
ar = matrix(c(1, 0.5, -0.1, 0.1), nrow = 2, ncol = 2)
d = c(0.1, 0.1)
sigma = 0.5
sfarima_model = list(ar = ar, ma = ma, d = d, sigma = sigma)
sfarima_sim = sfarima.sim(100, 100, model = sfarima_model)
summary(sfarima_sim)
sfarima_est = sfarima.est(sfarima_sim$Y)
summary(sfarima_est)