| causeSummary2NoP {generalCorr} | R Documentation |
No Print version Kernel causality summary of evidence for causal paths from three criteria using new exact stochastic dominance.
Description
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.
Usage
causeSummary2NoP(mtx, nam = colnames(mtx), ctrl = 0, dig = 6)
Arguments
mtx |
The data matrix with many columns, y the first column is fixed and then paired with all columns, one by one, and still called x for the purpose of flipping. |
nam |
vector of column names for |
ctrl |
data matrix for designated control variable(s) outside causal paths |
dig |
Number of digits for reporting (default |
Details
The algorithm determines causal path directions from the sign of the strength index and strength index values by comparing three aspects of flipped kernel regressions: [x1 on f(x2, x3, .. xp)] and its flipped version [x2 on f(x1, x3, .. xp)] We compare (i) formal exogeneity test criterion, (ii) absolute residuals, and (iii) R-squares of the flipped regressions implying three criteria Cr1, to Cr3. The criteria are quantified by newer exact methods using four orders of stochastic dominance, SD1 to SD4. See Vinod's (2021) SSRN papers. In portfolio applications of stochastic dominance, one wants higher values. Here, we are comparing two probability distributions of absolute residuals for two flipped models. We choose that flip, which has smaller absolute residuals that will have a better fit.
Value
If there are p columns in the input matrix, x1, x2, .., xp, say,
and if we keep x1 as a common member of all causal direction pairs
(x1, x(1+j)) for (j=1, 2, .., p-1) which can be flipped. That is, either x1 is
the cause or x(1+j) is the cause in a chosen pair.
The control
variables are not flipped. The printed output of this function
reports the results for p-1 pairs indicating which variable
(by name) causes which other variable (also by name).
It also prints a signed summary strength index in the range [-100,100].
A positive sign of the strength index means x1 kernel causes x(1+j),
whereas a negative strength index means x(1+j) kernel causes x1. The function
also prints the Pearson correlation and its p-value. In short, function returns
a matrix of p-1 rows and 5 columns entitled:
“cause", “response", “strength", “corr." and “p-value", respectively
with self-explanatory titles. The first two columns have names of variables
x1 or x(1+j), depending on which is the cause. The ‘strength’ column
reports the absolute value of the summary index, in the range [0,100],
providing a summary of causal results
based on the preponderance of evidence from Cr1 to Cr3
from four orders of stochastic dominance, moments, deciles,
etc. The order of input columns in mtx matters.
The fourth column, ‘corr.’ of ‘out’, reports the Pearson correlation coefficient.
The fifth column has the p-value for testing the null of zero Pearson coeff.
This function calls silentPair2, allowing for control variables.
The output of this function can be sent to ‘xtable’ for a nice Latex table.
Note
The European Crime data has all three criteria correctly suggesting that a
high crime rate kernel causes the deployment of a large number of police officers.
Since Cr1 to Cr3 nearly unanimously suggest ‘crim’ as the cause of ‘off’,
strength index 100 suggests unanimity among the criteria.
attach(EuroCrime); causeSummary(cbind(crim,off))
Author(s)
Prof. H. D. Vinod, Economics Dept., Fordham University, NY.
References
Vinod, H. D. 'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, doi:10.1080/03610918.2015.1122048
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.
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, Hrishikesh D., R Package GeneralCorr Functions for Portfolio Choice (November 11, 2021). Available at SSRN: https://ssrn.com/abstract=3961683
Vinod, Hrishikesh D., Stochastic Dominance Without Tears (January 26, 2021). Available at SSRN: https://ssrn.com/abstract=3773309
See Also
See siPair2Blk for a block version
See causeSummary is subject to trapezoidal approximation.
see silentPair2 called by this function.
Examples
## Not run:
mtx=as.matrix(mtcars[,1:3])
ctrl=as.matrix(mtcars[,4:5])
causeSummary2(mtx,ctrl,nam=colnames(mtx))
## End(Not run)
options(np.messages=FALSE)
set.seed(234)
z=runif(10,2,11)# z is independently created
x=sample(1:10)+z/10 #x is somewhat indep and affected by z
y=1+2*x+3*z+rnorm(10)
w=runif(10)
x2=x;x2[4]=NA;y2=y;y2[8]=NA;w2=w;w2[4]=NA
causeSummary2(mtx=cbind(x2,y2), ctrl=cbind(z,w2))