bootQuantile function

Compute confidence intervals [quantile(s)] of indexes from bootPairs output

Begin with the output of bootPairs function, a (n999 by p-1) matrix when there are p columns of data, bootQuantile produces a (k by p-1) mtx of quantile(s) of bootstrap ouput assuming that there are k quantiles needed.

bootQuantile(out, probs = c(0.025, 0.975), per100 = TRUE)

Arguments

• out: output from bootPairs with p-1 columns and n999 rows
• probs: quantile evaluation probabilities. The default is k=2, probs=c(.025,0.975) for a 95 percent confidence interval. Note that there are k=2 quantiles desired for each column with this specification
• per100: logical (default per100=TRUE) to change the range of 'sum' to [-100, 100] values which are easier to interpret

Returns

CI k quantiles evaluated at probs as a matrix with k rows and quantile of pairwise p-1 indexes representing p-1 column pairs (fixing the first column in each pair) This function summarizes 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)
bootQuantile(bb) #gives summary stats for n999 bootstrap sum computations

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

data('EuroCrime')
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.
bootQuantile(bb)# quantile matrix for 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.

Author(s)

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