parcorMtx: Matrix of generalized partial correlation coefficients,...

In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

Matrix of generalized partial correlation coefficients, always leaving out control variables, if any.

Description

This function calls parcor_ijk function which uses original data to compute generalized partial correlations between X_i and X_j where j can be any one of the remaining variables in the input matrix mtx. Partial correlations remove the effect of variables x_k other than X_i and X_j. Calculation further allows for the presence of control variable(s) (if any) to remain always outside the input matrix and whose effect is also removed in computing partial correlations.

Usage

parcorMtx(mtx, ctrl = 0, dig = 4, verbo = FALSE)

Arguments

mtx	Input data matrix with p columns. p is at least 3 columns.
ctrl	Input vector or matrix of data for control variable(s), default is ctrl=0 when control variables are absent
dig	The number of digits for reporting (=4, default)
verbo	Make this TRUE for detailed printing of computational steps

Value

A p by p ‘out’ matrix containing partials r*(i,j | k). and r*(j,i | k).

Note

We want to get all partial correlation coefficient pairs removing other column effects. Vinod (2018) shows why one needs more than one criterion to decide the causal paths or exogeneity.

Author(s)

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

References

Vinod, H. D. 'Generalized Correlations and Instantaneous Causality for Data Pairs Benchmark,' (March 8, 2015) https://www.ssrn.com/abstract=2574891

Vinod, H. D. 'Matrix Algebra Topics in Statistics and Economics Using R', Chapter 4 in Handbook of Statistics: Computational Statistics with R, Vol.32, co-editors: M. B. Rao and C.R. Rao. New York: North Holland, Elsevier Science Publishers, 2014, pp. 143-176.

Vinod, H. D. 'New Exogeneity Tests and Causal Paths,' (June 30, 2018). Available at SSRN: https://www.ssrn.com/abstract=3206096

See Also

See Also parcor_ijk.

Examples

set.seed(234)
z=runif(10,2,11)# z is independently created
x=sample(1:10)+z/10  #x is partly indep and partly affected by z
y=1+2*x+3*z+rnorm(10)# y depends on x and z not vice versa
mtx=cbind(x,y,z)
parcorMtx(mtx)
 
   
## Not run: 
set.seed(34);x=matrix(sample(1:600)[1:99],ncol=3)
colnames(x)=c('V1', 'v2', 'V3')
parcorMtx(x)

## End(Not run)

generalCorr documentation built on Oct. 10, 2023, 1:06 a.m.