abs_stdapdC

1) standardize the data to force mean zero and variance unity, 2) kernel
regress x on y and a matrix of control variables, 
with the option `gradients = TRUE' and finally 3) compute
the absolute values of gradients

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

abs_stdapdC function

<dl><dt>x</dt>
<dd>vector of data on the dependent variable</dd>
<dt>y</dt>
<dd>data on the regressors which can be a matrix</dd>
<dt>ctrl</dt>
<dd>Data matrix on the control variable(s) beyond causal path issues</dd></dl>

Arguments

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

Author

Absolute values of gradients (apd's) of kernel regressions of x on y when
both x and y are standardized and control variables are present. — abs_stdapdC

<dl>

<dt>x</dt>
<dd>vector of data on the dependent variable</dd>


<dt>y</dt>
<dd>data on the regressors which can be a matrix</dd>


<dt>ctrl</dt>
<dd>Data matrix on the control variable(s) beyond causal path issues</dd>

</dl>

abs_stdapdC: Absolute values of gradients (apd's) of kernel regressions of x on y when both x and y are standardized and control variables are present.

Description

Usage

Value

Arguments

Author

Details

See Also

Examples