# siPairsBlk: Block Version of silentPairs for causality scores with... In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

 siPairsBlk R Documentation

## Block Version of silentPairs for causality scores with control variables

### Description

Allowing input matrix of control variables and missing data, this function produces a 3 column matrix summarizing the results where the estimated signs of stochastic dominance order values (+1, 0, -1) are weighted by `wt=c(1.2,1.1, 1.05, 1)` to compute an overall result for all orders of stochastic dominance by a weighted sum for the criteria Cr1 and Cr2 and added to the Cr3 estimate as: (+1, 0, -1), always in the range [–3.175, 3.175].

### Usage

``````siPairsBlk(
mtx,
ctrl = 0,
dig = 6,
blksiz = 10,
wt = c(1.2, 1.1, 1.05, 1),
sumwt = 4
)
``````

### 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 for the purpose of flipping with x1. p must be 2 or more `ctrl` data matrix for designated control variable(s) outside causal paths default ctrl=0 which 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 matrix then blksiz=n. That is, no blocking is done `wt` Allows user to choose a vector of four alternative weights for SD1 to SD4. `sumwt` Sum of weights can be changed here =4(default).

### Details

The reason for slightly declining weights on the signs from SD1 to SD4 is simply that the local mean comparisons implicit in SD1 are known to be more reliable than local variance implicit in SD2, local skewness implicit in SD3 and local kurtosis implicit in SD4. The source of slightly declining sampling unreliability of higher moments is the higher power of the deviations from the mean needed in their computations. The summary results for all three criteria are reported in a vector of numbers internally called `crall`:

### 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 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, \Sexpr[results=rd]{tools:::Rd_expr_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 `bootPairs`, `silentMtx`

See `someCPairs`, `some0Pairs`

### Examples

``````

## Not run:
options(np.messages=FALSE)
colnames(mtcars[2:ncol(mtcars)])
siPairsBlk(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
siPairsBlk(mtx=cbind(x2,y2), ctrl=cbind(z,w2))

``````

generalCorr documentation built on May 1, 2023, 9:06 a.m.