This function is an alternative implementation of somePairs
which also lets the user choose one of three criteria to determine causal direction
by setting typ as 1, 2 or 3. This function reports results for
only one criterion at a time unlike the function some0Pairs which
summarizes the resulting causal directions for all criteria with suitable weights.
If some variables are `control' variables, use someCPairs,
where notation C=control.
somePairs2(mtx, dig = 6, verbo = FALSE, typ = 1, rnam = FALSE)A matrix containing causal identification results for one criterion.
The first column of the input mtx having p columns
is paired with (p-1) other columns The output matrix headings are
self-explanatory and distinct for each criterion Cr1 to Cr3.
The data matrix in the first column is paired with all others.
Number of digits for reporting (default dig=6).
Make verbo= TRUE for printing detailed steps.
Must be 1 (default), 2 or 3 for the three criteria.
Make rnam= TRUE if cleverly created rownames are desired.
Prof. H. D. Vinod, Economics Dept., Fordham University, NY
(typ=1) reports ('Y', 'X', 'Cause', 'SD1.rhserr', 'SD2.rhserr', 'SD3.rhserr', 'SD4.rhserr') naming variables identifying the 'cause,' using Hausman-Wu criterion. It measures of stochastic dominance using absolute values of kernel regression abs(RHS first regressor*residual), comparing flipped regressions X on Y versus Y on X.
(typ=2) reports ('Y', 'X', 'Cause', 'SD1res', 'SD2res', 'SD3res', 'SD4res') and measures of stochastic dominance using absolute values of kernel regression residuals comparing regression of X on Y with that of Y on X.
(typ=3) reports ('Y', 'X', 'Cause', 'r*X|Y', 'r*Y|X', 'r', 'p-val') containing generalized correlation coefficients r*, 'r' refers to the Pearson correlation coefficient and p-val column has the p-values for testing the significance of Pearson's 'r'.
H. D. Vinod 'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, tools:::Rd_expr_doi("10.1080/03610918.2015.1122048")
The related function some0Pairs may be more useful, since it
reports on all three criteria (by choosing typ=1,2,3) and
further summarizes their results by weighting to help choose causal paths.
Alternative and revised function somePairs2
implements the Cr1 (first criterion) with a direct estimate of
the Hausman-Wu statistic for testing exogeneity.
if (FALSE) {
data(mtcars)
somePairs2(mtcars)
}
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