probSign function

Compute probability of positive or negative sign from bootPairs output

If there are p columns of data, probSign 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.

probSign(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)
probSign(bb,tau=0.476) #gives summary stats for n999 bootstrap sum computations

bb=bootPairs(airquality,n999=999);options(np.messages=FALSE)
probSign(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.
probSign(bb,tau=0.476)#signs 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