# bootGcLC: Compute vector of n999 nonlinear Granger causality paths In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

 bootGcLC R Documentation

## 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. The LC in the function name stands for local constant. Kernel regression np package options regtype="lc" for local constant, and bwmethod="cv.ls" for least squares-based bandwidth selection are fixed.

### Usage

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

### 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)

### Value

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

### Note

This computation is computer intensive and generally very slow. It may be better to use this function it at a later stage in the investigation, after a preliminary causal determination is already made. The 3 outputs of GauseX12 are two Rsquares and the difference between after subtracting the second from the first. Col. 1 has (RsqX1onX2) Col.2 has (RsqX2onX1), and Col.3 has dif=(RsqX1onX2 -RsqX2onX1) Note that R-squares are always positive. If dif>0, RsqX1onX2>RsqX2onX1, implying that x2 on RHS performs better that is, x2 –> x1 is the path, or x2 Granger-causes x1. If dif<0, x1 –> x2 holds. If dif is too close to zero, we may have bidirectional causality x1 <–> x2. The proportion of resamples (out of n999) having dif<0 suggests level of confidence in the conclusion x1 –> x2. The proportion of resamples (out of n999) having dif>0 suggests level of confidence in the conclusion x2 –> x1.

### Author(s)

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, \Sexpr[results=rd]{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 `GcRsqX12c`.

### Examples

``````## Not run:
library(Ecdat);options(np.messages=FALSE);attach(data.frame(MoneyUS))
bootGcLC(y,m,n999=9)

## End(Not run)
## Not run:
library(lmtest); data(ChickEgg);attach(data.frame(ChickEgg))
b2=bootGcLC(x1=chicken,x2=egg,pwanted=3,px1=3,px2=3,n999=99)

## End(Not run)

``````

generalCorr documentation built on May 1, 2023, 9:06 a.m.