bootSign function

Probability of unambiguously correct (+ or -) sign from bootPairs output

If there are p columns of data, bootSign produces a p-1 by 1 vector of probabilities of correct signs assuming that the mean of n999 values has the correct sign and assuming that m of the 'sum' index values inside the range [-tau, tau] are neither positive nor negative but indeterminate or ambiguous (being too close to zero). That is, the denominator of P(+1) or P(-1) is (n999-m) if m signs are too close to zero. Thus it measures the bootstrap success rate in identifying the correct sign, when the sign of the average of n999 bootstraps is assumed to be correct.

bootSign(out, tau = 0.476)

Arguments

• out: output from bootPairs with p-1 columns and n999 rows
• tau: threshold to determine what value is too close to zero, default tau=0.476 is equivalent to 15 percent threshold for the unanimity index ui

Returns

sgn When mtx has p columns, sgn

reports pairwise p-1 signs representing (fixing the first column in each pair) the average sign after averaging the output of of bootPairs(mtx) (a n999 by p-1 matrix) each containing resampled sum' values summarizing the weighted sums associated with all three criteria from the function silentPairs(mtx)`

applied to each bootstrap sample separately. #'

Examples

## Not run:

options(np.messages = FALSE)
set.seed(34);x=sample(1:10);y=sample(2:11)
bb=bootPairs(cbind(x,y),n999=29)
bootSign(bb,tau=0.476) #gives success rate in n999 bootstrap sum computations

bb=bootPairs(airquality,n999=999);options(np.messages=FALSE)
bootSign(bb,tau=0.476)#signs for n999 bootstrap sum computations

data('EuroCrime');options(np.messages=FALSE)
attach(EuroCrime)
bb=bootPairs(cbind(crim,off),n999=29) #col.1= crim causes off 
#hence positive signs are more intuitively meaningful.
#note that n999=29 is too small for real problems, chosen for quickness here.
bootSign(bb,tau=0.476)#gives success rate in n999 bootstrap sum computations
## 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")

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 silentPairs, bootQuantile, bootSignPcent.

Author(s)

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