mfw_cov_eval: multivariate Fourier Whittle estimators

Description

Computes the multivariate Fourier Whittle estimator of the long-run covariance matrix (also called fractal connectivity) for a given value of long-memory parameters d.

Usage

mfw_cov_eval(d, x, m)

Arguments

vector of long-memory parameters (dimension should match dimension of x)

data (matrix with time in rows and variables in columns)

truncation number used for the estimation of the periodogram

Value

long-run covariance matrix estimation.

Details

The choice of m determines the range of frequencies used in the computation of the periodogram, $lambda_j = 2*pi*j/N$, $j$ = 1,... , m. The optimal value depends on the spectral properties of the time series such as the presence of short range dependence. In Shimotsu (2007), m is chosen to be equal to $N^0.65$.

References

K. Shimotsu (2007) Gaussian semiparametric estimation of multivariate fractionally integrated processes Journal of Econometrics Vol. 137, N. 2, pages 277-310.

S. Achard, I. Gannaz (2014) Multivariate wavelet Whittle estimation in long-range dependence. arXiv, http://arxiv.org/abs/1412.0391

Examples

Run this code

### Simulation of ARFIMA(0,\code{d},0)
rho <- 0.4
cov <- matrix(c(1,rho,rho,1),2,2)
d <- c(0.4,0.2)
J <- 9
N <- 2^J

resp <- varfima(N, d, cov_matrix=cov)
x <- resp$x
long_run_cov <- resp$long_run_cov

m <- 57 ## default value of Shimotsu
G <- mfw_cov_eval(d,x,m) # estimation of the covariance matrix when d is known

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