Generalized Correlations, Causal Paths and Portfolio Selection

---

Documentation for package ‘generalCorr’ version 1.2.6

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

A B C D E G H I J K M N O P R S W

generalCorr-package

generalCorr package description:

-- A --

absBstdres	Block version of abs-stdres Absolute values of residuals of kernel regressions of standardized x on standardized y, no control variables.
absBstdresC	Block version of Absolute values of residuals of kernel regressions of standardized x on standardized y and control variables.
absBstdrhserC	Block version abs_stdrhser Absolute residuals kernel regressions of standardized x on y and control variables, Cr1 has abs(Resid*RHS).
abs_res	Absolute residuals of kernel regression of x on y.
abs_stdapd	Absolute values of gradients (apd's) of kernel regressions of x on y when both x and y are standardized.
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.
abs_stdres	Absolute values of residuals of kernel regressions of x on y when both x and y are standardized.
abs_stdresC	Absolute values of residuals of kernel regressions of x on y when both x and y are standardized and control variables are present (C for control presence).
abs_stdrhserC	Absolute residuals kernel regressions of standardized x on y and control variables, Cr1 has abs(RHS*y) not gradients.
abs_stdrhserr	Absolute values of Hausman-Wu null in kernel regressions of x on y when both x and y are standardized.
allPairs	Report causal identification for all pairs of variables in a matrix (deprecated function). It is better to choose a target variable and pair it with all others, instead of considering all possible targets.

-- B --

badCol	internal badCol
bigfp	Compute the numerical integration by the trapezoidal rule.
bootDom12	bootstrap confidence intervals for (x2-x1) exact SD1 to SD4 stochastic dominance .
bootGcLC	Compute vector of n999 nonlinear Granger causality paths
bootGcRsq	Compute vector of n999 nonlinear Granger causality paths
bootPair2	Compute matrix of n999 rows and p-1 columns of bootstrap 'sum' (scores from Cr1 to Cr3).
bootPairs	Compute matrix of n999 rows and p-1 columns of bootstrap 'sum' (strength from Cr1 to Cr3).
bootPairs0	Compute matrix of n999 rows and p-1 columns of bootstrap 'sum' index (strength from older criterion Cr1, with newer Cr2 and Cr3).
bootQuantile	Compute confidence intervals [quantile(s)] of indexes from bootPairs output
bootSign	Probability of unambiguously correct (+ or -) sign from bootPairs output
bootSignPcent	Probability of unambiguously correct (+ or -) sign from bootPairs output transformed to percentages.
bootSummary	Compute usual summary stats of 'sum' indexes from bootPairs output
bootSummary2	Compute usual summary stats of 'sum' index in (-100, 100) from bootPair2

-- C --

canonRho	Generalized canonical correlation, estimating alpha, beta, rho.
causeAllPair	All Pair Version Kernel (block) causality summary paths from three criteria
causeSum2Blk	Block Version 2: Kernel causality summary of causal paths from three criteria
causeSum2Panel	Kernel regressions based causal paths in Panel Data.
causeSummary	Kernel causality summary of evidence for causal paths from three criteria
causeSummary0	Older Kernel causality summary of evidence for causal paths from three criteria
causeSummary2	Kernel causality summary of evidence for causal paths from three criteria using new exact stochastic dominance. The function develops a unanimity index for deciding which flip (y on xi) or (xi on y) is best. Relevant signs determine the causal direction and unanimity index among three criteria. While allowing the researcher to keep some variables as controls, or outside the scope of causal path determination (e.g., age or latitude) this function produces detailed causal path information in a 5 column matrix identifying the names of variables, causal path directions, path strengths re-scaled to be in the range [-100, 100], (table reports absolute values of the strength) plus Pearson correlation and its p-value. The '2' in the name of the function suggests a second implementation where exact stochastic dominance, decileVote, and momentVote are used and where we avoid Anderson's trapezoidal approximation.
causeSummary2NoP	No Print version Kernel causality summary of evidence for causal paths from three criteria using new exact stochastic dominance.
causeSummBlk	Block Version 2: Kernel causality summary of causal paths from three criteria
causeSumNoP	No print (NoP) version of causeSummBlk summary causal paths from three criteria
cofactor	Compute cofactor of a matrix based on row r and column c.
compPortfo	Compares two vectors (portfolios) using momentVote, DecileVote and exactSdMtx functions.
comp_portfo2	Compares two vectors (portfolios) using stochastic dominance of orders 1 to 4.

-- D --

da	internal da
da2Lag	internal da2Lag
decileVote	Function compares nine deciles of stock return distributions.
depMeas	depMeas Signed measure of nonlinear nonparametric dependence between two vectors.
dif4	order 4 differencing of a time series vector
dif4mtx	order four differencing of a matrix of time series
diff.e0	Internal diff.e0
dig	Internal dig

-- E --

e0	internal e0
EuroCrime	European Crime Data
exactSdMtx	Exact stochastic dominance computation from areas above ECDF pillars.

-- G --

GcRsqX12	Generalized Granger-Causality. If dif>0, x2 Granger-causes x1.
GcRsqX12c	Generalized Granger-Causality. If dif>0, x2 Granger-causes x1.
GcRsqYX	Nonlinear Granger causality between two time series workhorse function.
GcRsqYXc	Nonlinear Granger causality between two time series workhorse function.(local constant version)
generalCorrInfo	generalCorr package description:
get0outliers	Function to compute outliers and their count using Tukey's method using 1.5 times interquartile range (IQR) to define boundaries.
getSeq	Two sequences: starting+ending values from n and blocksize (internal use)
gmc0	internal gmc0
gmc1	internal gmc1
gmcmtx0	Matrix R* of generalized correlation coefficients captures nonlinearities.
gmcmtxBlk	Matrix R* of generalized correlation coefficients captures nonlinearities using blocks.
gmcmtxZ	compute the matrix R* of generalized correlation coefficients.
gmcxy_np	Function to compute generalized correlation coefficients r*(x|y) and r*(y|x) from two vectors (not matrices)
goodCol	internal goodCol

-- H --

heurist

Heuristic t test of the difference between two generalized correlations.

-- I --

i	internal i
ibad	internal object
ii	internal ii

-- J --

j	internal j

-- K --

kern	Kernel regression with options for residuals and gradients.
kern2	Kernel regression version 2 with optional residuals and gradients with regtype="ll" for local linear, bwmethod="cv.aic" for AIC-based bandwidth selection.
kern2ctrl	Kernel regression with control variables and optional residuals and gradients. version 2 regtype="ll" for local linear, bwmethod="cv.aic" for AIC-based bandwidth selection. It admits control variables.
kern_ctrl	Kernel regression with control variables and optional residuals and gradients.

-- M --

mag	Approximate overall magnitudes of kernel regression partials dx/dy and dy/dx.
mag_ctrl	After removing control variables, magnitude of effect of x on y, and of y on x.
min.e0	internal min.e0
minor	Function to do compute the minor of a matrix defined by row r and column c.
momentVote	Function compares Pearson Stats and Sharpe Ratio for a matrix of stock returns
mtx	internal mtx
mtx0	internal mtx0
mtx2	internal mtx2

-- N --

n	internal n
nall	internal nall
nam.badCol	internal nam.badCol
nam.goodCol	internal nam.goodCol
nam.mtx0	internal nam.mtx0
napair	Function to do pairwise deletion of missing rows.
naTriple	Function to do matched deletion of missing rows from x, y and z variable(s).
naTriplet	Function to do matched deletion of missing rows from x, y and control variable(s).
NLhat	Compute fitted values from kernel regression of x on y and y on x

-- O --

out1	internal out1
outOFsamp	Compare out-of-sample portfolio choice algorithms by a leave-percent-out method.
outOFsell	Compare out-of-sample (short) selling algorithms by a leave-percent-out method.

-- P --

p1	internal p1
Panel2Lag	Function to compute a vector of 2 lagged values of a variable from panel data.
PanelLag	Function for computing a vector of one-lagged values of xj, a variable from panel data.
parcorBijk	Block version of generalized partial correlation coefficients between Xi and Xj, after removing the effect of xk, via nonparametric regression residuals.
parcorBMany	Block version reports many generalized partial correlation coefficients allowing control variables.
parcorHijk	Generalized partial correlation coefficients between Xi and Xj, after removing the effect of Xk, via OLS regression residuals.
parcorHijk2	Generalized partial correlation coefficients between Xi and Xj,
parcorMany	Report many generalized partial correlation coefficients allowing control variables.
parcorMtx	Matrix of generalized partial correlation coefficients, always leaving out control variables, if any.
parcorSilent	Silently compute generalized (ridge-adjusted) partial correlation coefficients from matrix R*.
parcorVec	Vector of generalized partial correlation coefficients (GPCC), always leaving out control variables, if any.
parcorVecH	Vector of hybrid generalized partial correlation coefficients.
parcorVecH2	Vector of hybrid generalized partial correlation coefficients.
parcor_ijk	Generalized partial correlation coefficients between Xi and Xj, after removing the effect of xk, via nonparametric regression residuals.
parcor_ijkOLD	Generalized partial correlation coefficient between Xi and Xj after removing the effect of all others. (older version, deprecated)
parcor_linear	Partial correlation coefficient between Xi and Xj after removing the linear effect of all others.
parcor_ridg	Compute generalized (ridge-adjusted) partial correlation coefficients from matrix R*. (deprecated)
pcause	Compute the bootstrap probability of correct causal direction.
pillar3D	Create a 3D pillar chart to display (x, y, z) data coordinate surface.
prelec2	Intermediate weighting function giving Non-Expected Utility theory weights.
probSign	Compute probability of positive or negative sign from bootPairs output

-- R --

rank2return	Compute the portfolio return knowing the rank of a stock in the input 'mtx'.
rank2sell	Compute the portfolio return knowing the rank of a stock in the input 'mtx'. This function computes the return earned knowing the rank of a stock computed elsewhere and named myrank associate with the data columns in the input mtx of stock returns. For example, mtx has p=28 Dow Jones stocks over n=169 monthly returns. Portfolio weights are assumed to be linearly declining. If maxChosen=4, the weights are 1/10, 2/10, 3/10 and 4/10, which add up to unity. These portfolio weights are assigned in their order in the sense that first chosen stock (choice rank =p) gets portfolio weight=4/10. The function computes return from the stocks using the 'myrank' argument. This helps in assessing out-of-sample performance of (short) the strategy of selling lowest ranking stocks. It is mostly for internal use by 'outOFsell()'. This is a sell version of 'rank2return()'.
rhs.lag2	internal rhs.lag2
rhs1	internal rhs1
ridgek	internal ridgek
rij	internal rij
rijMrji	internal rijMrji
rji	internal rji
rrij	internal rrij
rrji	internal rrji
rstar	Function to compute generalized correlation coefficients r*(x,y).

-- S --

sales2Lag	internal sales2Lag
salesLag	internal salesLag
seed	internal seed
sgn.e0	internal sgn.e0
silentMtx	No-print kernel-causality unanimity score matrix with optional control variables
silentMtx0	Older kernel-causality unanimity score matrix with optional control variables
silentPair2	kernel causality (version 2) scores with control variables
silentPairs	No-print kernel causality scores with control variables Hausman-Wu Criterion 1
silentPairs0	Older version, kernel causality weighted sum allowing control variables
siPair2Blk	Block Version of silentPair2 for causality scores with control variables
siPairsBlk	Block Version of silentPairs for causality scores with control variables
some0Pairs	Function reporting detailed kernel causality results in a 7-column matrix (uses deprecated criterion 1, no longer recommended but may be useful for second and third criterion typ=2,3)
someCPairs	Kernel causality computations admitting control variables.
someCPairs2	Kernel causality computations admitting control variables reporting a 7-column matrix, version 2.
someMagPairs	Summary magnitudes after removing control variables in several pairs where dependent variable is fixed.
somePairs	Function reporting kernel causality results as a 7-column matrix.(deprecated)
somePairs2	Function reporting kernel causality results as a 7-column matrix, version 2.
sort.abse0	internal sort.abse0
sort.e0	internal sort.e0
sort_matrix	Sort all columns of matrix x with respect to the j-th column.
stdres	Residuals of kernel regressions of x on y when both x and y are standardized.
stdz_xy	Standardize x and y vectors to achieve zero mean and unit variance.
stochdom2	Compute vectors measuring stochastic dominance of four orders.
sudoCoefParcor	Pseudo regression coefficients from generalized partial correlation coefficients, (GPCC).
sudoCoefParcorH	Peudo regression coefficients from hybrid generalized partial correlation coefficients (HGPCC).
summaryRank	Compute ranks of rows of matrix and summarize them into a choice suggestion.
symmze	Replace asymmetric matrix by max of abs values of [i,j] or [j,i] elements.

-- W --

wtdpapb

Creates input for the stochastic dominance function stochdom2