generalCorrInfo: generalCorr package description:

Description Details Note References

Description

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.

Details

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.

Note

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.

References

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.