ARMA: Create an Autoregressive Moving Average (ARMA) Process In gmwm: Generalized Method of Wavelet Moments

Description

Sets up the necessary backend for the ARMA process.

Usage

 1 ARMA(ar = 1, ma = 1, sigma2 = 1)

Arguments

 ar A vector or integer containing either the coefficients for phi's or the process number p for the Autoregressive (AR) term. ma A vector or integer containing either the coefficients for theta's or the process number q for the Moving Average (MA) term. sigma2 A double value for the standard deviation, sigma, of the ARMA process.

Details

A standard deviation is required since the model generation statements utilize randomization functions expecting a standard deviation instead of a variance.

Value

An S3 object with called ts.model with the following structure:

process.desc

AR x p, MA x q

theta

sigma

plength

Number of Parameters

obj.desc

y desc replicated x times

obj

Depth of Parameters e.g. list(c(length(ar),length(ma),1) )

starting

Guess Starting values? TRUE or FALSE (e.g. specified value)

JJB

Examples

 1 2 3 4 5 6 7 # Create an ARMA(1,2) process ARMA(ar=1,2) # Creates an ARMA(3,2) process with predefined coefficients. ARMA(ar=c(0.23,.43, .59), ma=c(0.4,.3)) # Creates an ARMA(3,2) process with predefined coefficients and standard deviation ARMA(ar=c(0.23,.43, .59), ma=c(0.4,.3), sigma2 = 1.5)

gmwm documentation built on April 14, 2017, 4:38 p.m.