sudoCoefParcorH: Peudo regression coefficients from hybrid generalized partial correlation coefficients (HGPCC).

Description

This function gets HGPCCs by calling parcorVecH function. Pseudo regression coefficient of a kernel regression is obtained by HGPCC*(sd dep.var)/(sd regressor), that is multiplying the HGPCC by the standard deviation (sd) of the dependent variable and dividing by the sd of the regressor.

Usage

sudoCoefParcorH(mtx, ctrl = 0, verbo = FALSE, idep = 1)

Value

A p by 1 `out' vector pseudo partial derivatives

Arguments

mtx: Input data matrix with p (> or = 3) columns,
ctrl: Input vector or matrix of data for control variable(s), default is ctrl=0 when control variables are absent
verbo: Make this TRUE for detailed printing of computational steps
idep: The column number of the dependent variable (=1, default)

Author

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

Vinod, H. D. (2021) 'Generalized, Partial and Canonical Correlation Coefficients' Computational Economics, 59(1), 1--28.

Examples

Run this code

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)
sudoCoefParcor(mtx, idep=2)
 
   
if (FALSE) {
set.seed(34);x=matrix(sample(1:600)[1:99],ncol=3)
colnames(x)=c('V1', 'v2', 'V3')#some names needed
sudoCoefParcorH(x)
}

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