Description Details Note References

This package provides convenient software tools for causal path determinations
using Vinod (2014, 2015) and extends them.
A matrix of asymmetric generalized correlations r*(x|y) is reported by the
functions `rstar`

and `gmcmtx0`

. The r*(x|y)
measures the strength of the
dependence of x on y. If |r*(x|y)|> |r*(y|x)| it suggests that y is more likely
the "kernel cause" of x. This package refers to
the r* based criterion as criterion 3 (Cr3) and further adds
two additional ways of comparing
two kernel regressions helping identify the ‘cause’ called criterion 1 and 2
(Cr1 and Cr2) using absolute values of gradients and residuals, respectively.
See references below. The package has one-line commands summarizing all three criteria
leading to high (over 70 %) success rates in causal path identifications.

The usual partial correlations are generalized for the asymmetric matrix of r*'s.
Partial correlations help asses the effect of x on y after removing the effect of a
set of (control) variables. See `parcor_ijk`

and `parcor_ridg`

.
Another way of generalizing partial correlations
by using incremental R-square values in kernel regressions are provided in functions
`mag_ctrl`

and `someMagPairs`

.

The package provides additional tools for causal assessment,
for printing the causal detections in a clear, comprehensive compact summary form,
such as `somePairs`

, `some0Pairs`

, `someCPairs`

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 function `causeSummary`

gives an overall summary of causal path results.
The compact function `silentPairs`

gives one-line summary of causal path strengths,
where negative strength means that variable ‘causes’ the variable in the first column.

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.

In simultaneous equation
models where endogeneity of regressors is feared, we suggest using Prof. Koopmans' method
which suggests ignoring endogeneity issues for all variables “causing” the dependent variable
assessed by our three criteria. Weighted summary of all three criteria is in `someCPairs`

.

A vignette1 provided with this package generalCorr at CRAN
describes the usage of the package with examples. Type the following command:
`vignette("generalCorr-vignette", package="generalCorr")`

to read the vignette.
See also additional citations in the vignette, the references here and their citations
for further details.

Vinod, H. D.'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, http://dx.doi.org/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. Causal Paths and Exogeneity Tests in Generalcorr Package for Air Pollution and Monetary Policy (June 6, 2017). Available at SSRN: https://ssrn.com/abstract=2982128#'

generalCorr documentation built on July 13, 2018, 9:03 a.m.

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