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 conditional Granger-causality
spectrum. Defaults to FALSE.
parameter p to be passed to function VAR.
It corresponds to the number of lags of the second VAR model. Defaults to 0.
conf
prescribed confidence level. It defaults to 0.95.
Value
frequency: frequencies used by Fast Fourier Transform.
n: time series length.
confidence_level: prescribed confidence level.
significant_frequencies: frequencies at which the test is significant..
F-test: computed F-test at each frequency.
F-threshold: F-threshold at each frequency under prescribed confidence level.
roots: roots of the estimated VAR model.
delays: delays of the estimated VAR model.
The result is returned invisibly if plot is TRUE.
Details
bc_test_uncond calculates the test of Breitung and Candelon (2006) on the unconditional Granger-causality
of a time series x (effect variable) respect to a time series y (cause variable).
It requireNamespaces package vars.
References
Breitung, J., Candelon, B., 2006. Testing for short- and long-run causality: A frequency-domain approach.
Journal of Econometrics. 132, 2, 363--378.
Farne', M., Montanari, A., 2018. A bootstrap test to detect prominent Granger-causalities across frequencies.
<arXiv:1803.00374>, Submitted.
# NOT RUN {RealGdp.rate.ts<-euro_area_indicators[,1]
m3.rate.ts<-euro_area_indicators[,2]
uncond_bc<-bc_test_uncond(RealGdp.rate.ts,m3.rate.ts,ic.chosen="SC",max.lag=2)
# }