pcause function

Compute the bootstrap probability of correct causal direction.

Maximum entropy bootstrap (`meboot') package is used for statistical inference regarding $\delta$ which equals GMC(X|Y)-GMC(Y|X) defined by Zheng et al (2012). The bootstrap provides an approximation to chances of correct determination of the causal direction.

pcause(x, y, n999 = 999)

Arguments

• x: Vector of x data
• y: Vector of y data
• n999: Number of bootstrap replications (default=999)

Returns

P(cause) the bootstrap proportion of correct causal determinations.

Note

'pcause' is computer intensive and generally slow. It is better to use it at a later stage in the investigation when a preliminary causal determination is already made. Its use may slow the exploratory phase. In my experience, if P(cause) is less than 0.55, there is a cause for concern.

Examples

## Not run:

set.seed(34);x=sample(1:10);y=sample(2:11)
pcause(x,y,n999=29)

data('EuroCrime')
attach(EuroCrime)
pcause(crim,off,n999=29)
## End(Not run)

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.

Author(s)

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