Compute Theoretical ACF for an ARMA Process

Compute the theoretical autocorrelation function for an ARMA process.

Usage

ARMAacf_cpp(ar,ma,lag_max)

Arguments

ar

A vector of length p containing AR coefficients

ma

A vector of length q containing MA coefficients

lag_max

A unsigned integer indicating the maximum lag necessary

Value

x A matrix listing values from 1...nx in one column and 1...1, 2...2,....,n...n, in the other

Details

This is an implementaiton of the ARMAacf function in R. It is approximately 40x times faster. The benchmark was done on iMac Late 2013 using vecLib as the BLAS.

Author

R Core Team and JJB

Examples

# ARMA(2,1)
ARMAacf_cpp(c(1.0, -0.25), 1.0, lag_max = 10)
#>              [,1]
#>  [1,] 1.000000000
#>  [2,] 0.875000000
#>  [3,] 0.625000000
#>  [4,] 0.406250000
#>  [5,] 0.250000000
#>  [6,] 0.148437500
#>  [7,] 0.085937500
#>  [8,] 0.048828125
#>  [9,] 0.027343750
#> [10,] 0.015136719
#> [11,] 0.008300781
# ARMA(0,1)
ARMAacf_cpp(numeric(0), .35, lag_max = 10)
#>           [,1]
#>  [1,] 1.000000
#>  [2,] 0.311804
#>  [3,] 0.000000
#>  [4,] 0.000000
#>  [5,] 0.000000
#>  [6,] 0.000000
#>  [7,] 0.000000
#>  [8,] 0.000000
#>  [9,] 0.000000
#> [10,] 0.000000
#> [11,] 0.000000