GcRsqX12c function

Generalized Granger-Causality. If dif>0, x2 Granger-causes x1.

Generalized Granger-Causality. If dif>0, x2 Granger-causes x1.

The usual Granger-causality assumes linear regressions. This allows nonlinear nonparametric kernel regressions using a local constat (lc) option. Calls GcRsqYXc for R square from kernel regression. R^2[x1=f(x1,x2)] choosing GcRsqYXc(y=x1, x=x2). The name c' in the function refers to local constant option of kernel regressions. It predicts x1 from both x1 and x2 using all information till time (t-1). It also calls GcRsqYXc again after flipping x1 and x2. It returns RsqX1onX2, RsqX2onX1 and the difference dif=(RsqX1onX2-RsqX2onX1) If (dif>0) the regression x1=f(x1,x2) is better than the flipped version implying that x1 is more predictable or x2 Granger-causes x1 x2 --> x1, rather than vice versa. The kernel regressions use regtype="lc" for local constant, bwmethod="cv.ls" for least squares-based bandwidth selection.

GcRsqX12c(x1, x2, px1 = 4, px2 = 4, pwanted = 4, ctrl = 0)

Arguments

  • x1: The data vector x1
  • x2: The data vector x2
  • px1: number of lags of x1 in the data default px1=4
  • px2: number of lags of x2 in the data, default px2=4
  • pwanted: number of lags of both x2 and x1 wanted for Granger causal analysis, default =4
  • ctrl: data matrix for designated control variable(s) outside causal paths default=0 means no control variables are present

Returns

This function returns 3 numbers: RsqX1onX2, RsqX2onX1 and dif

returns a list of 3 numbers. RsqX1onX2=(Rsquare of kernel regression of X1 on X1 and X2), RsqX2onX1= (Rsquare of kernel regression of x2 on X2 and X1), and the difference between the two Rquares called dif

Examples

## Not run: library(Ecdat);options(np.messages=FALSE);attach(data.frame(MoneyUS)) GcRsqX12c(y,m) ## End(Not run)

References

Vinod, H. D. `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")

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

Zheng, S., Shi, N.-Z., Zhang, Z., 2012. Generalized measures of correlation for asymmetry, nonlinearity, and beyond. Journal of the American Statistical Association 107, 1239-1252. -at-note internal routine

See Also

causeSummary

GcRsqYXc

Author(s)

Prof. H. D. Vinod, Economics Dept., Fordham University, NY.

  • Maintainer: H. D. Vinod
  • License: GPL (>= 2)
  • Last published: 2023-10-09

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