generalCorr (version 1.2.6)

bootGcRsq: Compute vector of n999 nonlinear Granger causality paths

Description

Maximum entropy bootstrap (meboot) package is used for statistical inference The bootstrap output can be analyzed to estimate an approximate confidence interval on sample-based direction of the causal path. Kernel regression np package options regtype="ll" for local linear, and bwmethod="cv.aic" for AIC-based bandwidth selection are fixed.

Usage

bootGcRsq(x1, x2, px2 = 4, px1 = 4, pwanted = 4, ctrl = 0, n999 = 9)

Value

out is n999 X 3 matrix for 3 outputs of GcauseX12 resampled

Arguments

x1

The data vector x1

x2

The data vector x2

px2

number of lags of x2 in the data, default px2=4

px1

number of lags of x1 in the data default px1=4

pwanted

number of lags of both x2 and x1 wanted for Granger causal analysis, default =4

ctrl

data matrix having control variable(s) if any

n999

Number of bootstrap replications (default=9)

Author

Prof. H. D. Vinod, Economics Dept., Fordham University, NY

References

Vinod, H. D. `Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, tools:::Rd_expr_doi("10.1080/03610918.2015.1122048")

Zheng, S., Shi, N.-Z., and Zhang, Z. (2012). Generalized measures of correlation for asymmetry, nonlinearity, and beyond. Journal of the American Statistical Association, vol. 107, pp. 1239-1252.

Vinod, H. D. and Lopez-de-Lacalle, J. (2009). 'Maximum entropy bootstrap for time series: The meboot R package.' Journal of Statistical Software, Vol. 29(5), pp. 1-19.

Vinod, H. D. Causal Paths and Exogeneity Tests in Generalcorr Package for Air Pollution and Monetary Policy (June 6, 2017). Available at SSRN: https://www.ssrn.com/abstract=2982128

See Also

See Also GcRsqX12.

Examples

if (FALSE) {
library(Ecdat);options(np.messages=FALSE);attach(data.frame(MoneyUS))
bootGcRsq(y,m,n999=9) 
}
if (FALSE) {
library(lmtest); data(ChickEgg);attach(data.frame(ChickEgg))
options(np.messages=FALSE)
b2=bootGcLC(x1=chicken,x2=egg,pwanted=3,px1=3,px2=3,n999=99)
Fn=function(x)quantile(x,prob=c(0.025, 0.975))#confInt
apply(b1,2,Fn)#reports 95 percent confidence interval
}