siPair2Blk: Block Version of silentPair2 for causality scores with...

Description Usage Arguments Value Note Author(s) References See Also Examples

View source: R/siPair2Blk.R

Description

Block version allows a new bandwidth (chosen by the np package) while fitting kernel regressions for each block of data. This may not be appropriate in all situations. Block size is flexible. The function develops a unanimity index regarding the particular flip (y on xi) or (xi on y) is best. Relevant signs determine the causal direction and unanimity index among three criteria. The ‘2’ in the name of the function suggests a second implementation where exact stochastic dominance, decileVote, and momentVote are used. It avoids Anderson's trapezoidal approximation. The summary results for all three criteria are reported in a vector of numbers internally called crall.

Usage

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siPair2Blk(mtx, ctrl = 0, dig = 6, blksiz = 10)

Arguments

mtx

The data matrix with p columns. Denote x1 as the first column, which is fixed and then paired with all other columns, say: x2, x3, .., xp, one by one flipping with x1.The number of columns, p, must be 2 or more

ctrl

data matrix for designated control variable(s) outside causal paths. The default ctrl=0 means that there are no control variables used.

dig

Number of digits for reporting (default dig=6).

blksiz

block size, default=10, if chosen blksiz >n, where n=rows in the matrix, then blksiz=n. That is, no blocking is done

Value

With p columns in mtx argument to this function, x1 can be paired with a total of p-1 columns (x2, x3, .., xp). Note we never flip any of the control variables with x1. This function produces i=1,2,..,p-1 numbers representing the summary sign, or ‘sum’ from the signs sg1 to sg3 associated with the three criteria: Cr1, Cr2 and Cr3. Note that sg1 and sg2 themselves are weighted signs using the weighted sum of signs from four orders of stochastic dominance. In general, a positive sign in the i-th location of the ‘sum’ output of this function means that x1 is the kernel cause while the variable in (i+1)-th column of mtx is the ‘effect’ or ‘response’ or ‘endogenous.’ The magnitude represents the strength (unanimity) of the evidence for a particular sign. Conversely, a negative sign in the i-th location of the ‘sum’ output of this function means that that the first variable listed as the input to this function is the ‘effect,’ while the variable in (i+1)-th column of mtx is the exogenous kernel cause.

Note

The European Crime data has all three criteria correctly suggesting that high crime rate kernel causes the deployment of a large number of police officers. The command attach(EuroCrime); silentPairs(cbind(crim,off)) returns only one number: 3.175, implying the highest unanimity strength index, with the positive sign suggesting ‘crim’ in the first column kernel causes ‘off’ in the second column of the argument mtx to this function.

Author(s)

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

References

H. D. Vinod 'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, doi: 10.1080/03610918.2015.1122048

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 bootPairs, silentMtx

See someCPairs, compPortfo

Examples

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## Not run: 
options(np.messages=FALSE)
colnames(mtcars[2:ncol(mtcars)])
siPair2Blk(mtcars[,1:3],ctrl=mtcars[,4:5]) # mpg paired with others

## End(Not run)

options(np.messages=FALSE)
set.seed(234)
z=runif(10,2,11)# z is independently created
x=sample(1:10)+z/10 #x is somewhat indep and affected by z
y=1+2*x+3*z+rnorm(10)
w=runif(10)
x2=x;x2[4]=NA;y2=y;y2[8]=NA;w2=w;w2[4]=NA
siPair2Blk(mtx=cbind(x2,y2), ctrl=cbind(z,w2))

generalCorr documentation built on Jan. 4, 2022, 1:08 a.m.