parcorBijk

This function uses data on two column vectors, xi, xj and a third
xk which can be a vector or a matrix, usually of the remaining 
variables in the model, including control variables, if any.
It first removes missing data from all input variables. Then,
it computes residuals of kernel regression (xi on xk) and (xj on xk). 
This is a block version of parcor_ijk.

Function gmcmtx0() computes a more reliable (general)
correlation matrix. Since causal paths from data are important for all sciences, the
package provides many sophisticated functions. causeSummBlk() and causeSum2Blk()
give easy-to-interpret causal paths. Let Z denote control variables and compare
two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1
says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent"
than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These
inequalities between many absolute values are quantified by four orders of
stochastic dominance. Our third criterion Cr3, for the causal path X to Y,
requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|.
The function parcorVec() reports generalized partials between the first
variable and all others. The package provides several R functions including
get0outliers() for outlier detection, bigfp() for numerical integration by the
trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts,
canonRho() for generalized canonical correlations, depMeas() measures nonlinear
dependence, and causeSummary(mtx) reports summary of causal paths among matrix
columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx()
can rank several stocks. Functions whose names begin with 'boot' provide bootstrap
statistical inference, including a new bootGcRsq() test for "Granger-causality"
allowing nonlinear relations. A new tool for evaluation of out-of-sample
portfolio performance is outOFsamp(). Panel data implementation is now included.
See eight vignettes of the package for theory, examples, and
usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}.

Hrishikesh Vinod

generalCorr

Generalized Correlations, Causal Paths and Portfolio Selection

parcorBijk function

<dl><dt>xi</dt>
<dd>Input vector of data for variable xi</dd>
<dt>xj</dt>
<dd>Input vector of data for variable xj</dd>
<dt>xk</dt>
<dd>Input data for variables in xk, usually control variables</dd>
<dt>blksiz</dt>
<dd>block size, default=10, if chosen blksiz &gt;n, where n=rows in matrix
then blksiz=n. That is, no blocking is done</dd></dl>

Arguments

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

Author

Block version of generalized partial correlation coefficients between Xi 
and Xj, after removing the
effect of xk, via nonparametric regression residuals. — parcorBijk

<dl>

<dt>xi</dt>
<dd>Input vector of data for variable xi</dd>


<dt>xj</dt>
<dd>Input vector of data for variable xj</dd>


<dt>xk</dt>
<dd>Input data for variables in xk, usually control variables</dd>


<dt>blksiz</dt>
<dd>block size, default=10, if chosen blksiz &gt;n, where n=rows in matrix
then blksiz=n. That is, no blocking is done</dd>

</dl>

Block version of generalized partial correlation coefficients between Xi 
and Xj, after removing the
effect of xk, via nonparametric regression residuals.

parcorBijk: Block version of generalized partial correlation coefficients between Xi and Xj, after removing the effect of xk, via nonparametric regression residuals.

Description

Usage

Value

Arguments

Author

See Also

Examples