generalCorrInfo: generalCorr package description:
In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection
generalCorrInfo R Documentation
generalCorr package description:
Description
This package provides convenient software tools for causal path determinations
using Vinod (2014, 2015, 2018, 2021) and is explained in many package vignettes.
causeSummary(mtx), causeSummary2(mtx),causeSum2Blk(mtx),
causeSummBlk are various versions reporting pair-wise causal
path directions and causal strengths. We fit
a kernel regression of X1 on (X2, X3,..Xk) and another flipped regression
of X2 on (X1, x3, ..Xk). We compare the two fits using three sophisticated criteria
called Cr1 to Cr3. We rescale the
weighted sum of the quantified three criteria to the [-100, 100] range.
The sign of the weighted sum gives the direction of the causal path, and
the magnitude of the weighted sum gives the strength of the causal path.
A matrix of non-symmetric generalized correlations r*(x|y) is reported by the
functions rstar() and gmcmtx0().
sudoCoefParcor() computes pseudo kernel regression coefficients based on
generalized partial correlation coefficients (GPCC)
depMeas() a measure of nonlinear nonparametric dependence between two vectors.
parcorVec() has generalized partial correlation coefficients, Vinod (2021)
parcorVecH() has a hybrid version of the above (using HGPCC).
The usual partial correlations r(x,y|z) for regression of y on (x, z) measure
the effect of y on x after removing the effect of z, where z can have several variables.
Vinod (2021) suggests new generalized partial correlation coefficients (GPCC)
using kernel regressions, r*(x,y|z).
Details
The criterion Cr1 uses observable values of standard exogeneity test criterion,
namely, (kernel regression residual) times (regressor values)
Cr2 computes absolute values kernel regression residuals.
The quantification of Cr1 and Cr2 further uses four orders of stochastic
dominance measures.
Cr3 compares the R-square of the two fits.
The package provides additional tools for matrix algebra, such as
cofactor(), for outlier detection get0outlier(),
for numerical integration by the trapezoidal rule, stochastic dominance
stochdom2() and comp_portfo2(), etc.
The package has a function pcause() for bootstrap-based statistical
inference and another one
for a heuristic t-test called heurist(). Pairwise deletion of missing data
is done in napair(), while triplet-wise deletion is in naTriplet()
intended for use when control variable(s) are also present. If one has
panel data, functions PanelLag() and Panel2Lag() are relevant.
pillar3D provides 3-dimensional plots of data that look
more like surfaces, than usual plots with vertical pins.
Recent 2020 additions include canonRho() for generalized canonical
correlations, and many
functions for Granger causality between lagged time series including
GcRsqX12(), bootGcRsq() and GcRsqYXc().
Recent additions include several functions for portfolio choice.
causeSum2Panel() for panel data,
sudoCoefParcor() for pseudo regression coefficients for kernel regressions.
decileVote(), momentVote(), exactSdMtx() for exact
computation of stochastic dominance from ECDF areas. The newer stochastic
dominance tools are used in causeSummary2(mtx),causeSum2Blk(mtx)
dif4mtx()
computes growth, change in growth etc. up-to order 4 differencing of time series.
outOFsamp() and outOFsell() pandemic-proof
out-of-sample evaluation of portfolio returns using randomization.
causeSum2Panel() exploits panel data features for causal paths.
Note
Eight vignettes provided with this package at CRAN
describe the theory and usage of the package with examples. Read them using
the command:
vignette("generalCorr-vignette") to read the first vignette.
vignettes 2 to 6 can be read by including the vignette number. For
example,
vignette("generalCorr-vignette6") to read the sixth vignette.
References
Vinod, H. D.'Generalized Correlation and Kernel Causality with
Applications in Development Economics' in Communications in
Statistics -Simulation and Computation, 2015,
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/03610918.2015.1122048")}
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.
Zheng, S., Shi, N.-Z., and Zhang, Z. (2012). 'Generalized measures
of correlation for asymmetry, nonlinearity, and beyond,'
Journal of the American Statistical Association, vol. 107, pp. 1239-1252.
Vinod, H. D. (2021) 'Generalized, Partial and Canonical Correlation
Coefficients' Computational Economics, 59(1), 1–28.
Vinod, H. D. Causal Paths and Exogeneity Tests
in Generalcorr Package for Air Pollution and Monetary Policy
(June 6, 2017). Available at SSRN:
https://www.ssrn.com/abstract=2982128
Vinod, H. D. 'New exogeneity tests and causal paths,'
Chapter 2 in 'Handbook of Statistics: Conceptual Econometrics
Using R', Vol.32, co-editors: H. D. Vinod and C.R. Rao. New York:
North Holland, Elsevier Science Publishers, 2019, pp. 33-64.
generalCorr documentation built on Oct. 10, 2023, 1:06 a.m.
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| generalCorrInfo | R Documentation |
generalCorr package description:
Description
This package provides convenient software tools for causal path determinations
using Vinod (2014, 2015, 2018, 2021) and is explained in many package vignettes.
causeSummary(mtx), causeSummary2(mtx),causeSum2Blk(mtx),
causeSummBlk are various versions reporting pair-wise causal
path directions and causal strengths. We fit
a kernel regression of X1 on (X2, X3,..Xk) and another flipped regression
of X2 on (X1, x3, ..Xk). We compare the two fits using three sophisticated criteria
called Cr1 to Cr3. We rescale the
weighted sum of the quantified three criteria to the [-100, 100] range.
The sign of the weighted sum gives the direction of the causal path, and
the magnitude of the weighted sum gives the strength of the causal path.
A matrix of non-symmetric generalized correlations r*(x|y) is reported by the
functions rstar() and gmcmtx0().
sudoCoefParcor() computes pseudo kernel regression coefficients based on
generalized partial correlation coefficients (GPCC)
depMeas() a measure of nonlinear nonparametric dependence between two vectors.
parcorVec() has generalized partial correlation coefficients, Vinod (2021)
parcorVecH() has a hybrid version of the above (using HGPCC).
The usual partial correlations r(x,y|z) for regression of y on (x, z) measure
the effect of y on x after removing the effect of z, where z can have several variables.
Vinod (2021) suggests new generalized partial correlation coefficients (GPCC)
using kernel regressions, r*(x,y|z).
Details
The criterion Cr1 uses observable values of standard exogeneity test criterion,
namely, (kernel regression residual) times (regressor values)
Cr2 computes absolute values kernel regression residuals.
The quantification of Cr1 and Cr2 further uses four orders of stochastic
dominance measures.
Cr3 compares the R-square of the two fits.
The package provides additional tools for matrix algebra, such as
cofactor(), for outlier detection get0outlier(),
for numerical integration by the trapezoidal rule, stochastic dominance
stochdom2() and comp_portfo2(), etc.
The package has a function pcause() for bootstrap-based statistical
inference and another one
for a heuristic t-test called heurist(). Pairwise deletion of missing data
is done in napair(), while triplet-wise deletion is in naTriplet()
intended for use when control variable(s) are also present. If one has
panel data, functions PanelLag() and Panel2Lag() are relevant.
pillar3D provides 3-dimensional plots of data that look
more like surfaces, than usual plots with vertical pins.
Recent 2020 additions include canonRho() for generalized canonical
correlations, and many
functions for Granger causality between lagged time series including
GcRsqX12(), bootGcRsq() and GcRsqYXc().
Recent additions include several functions for portfolio choice.
causeSum2Panel() for panel data,
sudoCoefParcor() for pseudo regression coefficients for kernel regressions.
decileVote(), momentVote(), exactSdMtx() for exact
computation of stochastic dominance from ECDF areas. The newer stochastic
dominance tools are used in causeSummary2(mtx),causeSum2Blk(mtx)
dif4mtx()
computes growth, change in growth etc. up-to order 4 differencing of time series.
outOFsamp() and outOFsell() pandemic-proof
out-of-sample evaluation of portfolio returns using randomization.
causeSum2Panel() exploits panel data features for causal paths.
Note
Eight vignettes provided with this package at CRAN
describe the theory and usage of the package with examples. Read them using
the command:
vignette("generalCorr-vignette") to read the first vignette.
vignettes 2 to 6 can be read by including the vignette number. For
example,
vignette("generalCorr-vignette6") to read the sixth vignette.
References
Vinod, H. D.'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/03610918.2015.1122048")}
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.
Zheng, S., Shi, N.-Z., and Zhang, Z. (2012). 'Generalized measures of correlation for asymmetry, nonlinearity, and beyond,' Journal of the American Statistical Association, vol. 107, pp. 1239-1252.
Vinod, H. D. (2021) 'Generalized, Partial and Canonical Correlation Coefficients' Computational Economics, 59(1), 1–28.
Vinod, H. D. Causal Paths and Exogeneity Tests in Generalcorr Package for Air Pollution and Monetary Policy (June 6, 2017). Available at SSRN: https://www.ssrn.com/abstract=2982128
Vinod, H. D. 'New exogeneity tests and causal paths,' Chapter 2 in 'Handbook of Statistics: Conceptual Econometrics Using R', Vol.32, co-editors: H. D. Vinod and C.R. Rao. New York: North Holland, Elsevier Science Publishers, 2019, pp. 33-64.
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