Granger.unconditional: Unconditional Granger-causality estimation

In grangers: Inference on Granger-Causality in the Frequency Domain

Description

Unconditional Granger-causality spectrum was first defined in Geweke (1982). It measures the strength of the causal link from time series y to time series x and viceversa in the frequency domain. It needs to estimate a VAR model on x and y by package vars. For computational details we refer to Ding et al. (2006).

Usage

Granger.unconditional(x, y, ic.chosen = "SC", max.lag = min(4,
  length(x) - 1), plot = F, type.chosen = "none", p = 0)

Arguments

x	univariate time series.
y	univariate time series (of the same length of x).
ic.chosen	estimation method parameter ic to be passed to function VAR of package vars. Defaults to ”SC” (Schwarz criterion). Alternatives are c(''AIC'',''HQ'',''SC'',''FPE'').
max.lag	maximum number of lags lag.max to be passed to function VAR. Defaults to min(4, length(x) - 1).
plot	logical; if TRUE, it returns the plot of unconditional Granger-causality spectra on both directions. Defaults to FALSE.
type.chosen	parameter type to be passed to function VAR. Defaults to ''none''. Alternatives are c(''none'',''const'',''trend'').
p	parameter p to be passed to function VAR. Defaults to 0.

Details

Granger.unconditional calculates the Granger-causality unconditional spectrum of a time series x (effect variable) respect to a time series y (cause variable). It requireNamespaces package vars.

Value

frequency: frequencies used by Fast Fourier Transform.

n: time series length.

Unconditional_causality_y.to.x: computed unconditional Granger-causality from y to x.

Unconditional_causality_x.to.y: computed unconditional Granger-causality from x to y.

roots: the roots of the estimated VAR on x and y.

The result is returned invisibly if plot is TRUE.

Author(s)

Matteo Farne', Angela Montanari, [e-mail]

References

Geweke, J., 1982. Measurement of linear dependence and feedback between multiple time series. J. Am. Stat. Assoc. 77, 304–313.

Ding, M., Chen, Y., Bressler, S.L., 2006. Granger Causality: Basic Theory and Application to Neuroscience, Chap.17. Handbook of Time Series Analysis Recent Theoretical Developments and Applications.

Farne', M., Montanari, A., 2018. A bootstrap test to detect prominent Granger-causalities across frequencies. <arXiv:1803.00374>, Submitted.

See Also

VAR.

Examples

RealGdp.rate.ts<-euro_area_indicators[,1]
m3.rate.ts<-euro_area_indicators[,2]
uncond_m3<-Granger.unconditional(RealGdp.rate.ts,m3.rate.ts,"SC",4)

grangers documentation built on June 3, 2019, 5:05 p.m.