Run several normality tests on VAR-LiNGAM residuals at once

Convenience wrapper (analogous to the Moneta Gauss_Tests) that applies multiple normality tests to the residuals and returns a single table with one p-value column per method plus per-variable skewness and excess kurtosis. Methods whose optional package is unavailable are skipped with a warning.

Usage

test_varlingam_residual_normality_all(
  result,
  methods = c("shapiro", "ad", "lillie", "jb"),
  alpha = 0.05,
  on = c("innovations", "var")
)

Arguments

result

a VARLiNGAMResult from lingam_var()

methods

character vector of tests to run; any of "shapiro", "ks", "ad", "lillie", "jb" (default runs shapiro/ad/lillie/jb)

alpha

significance level (default 0.05)

on

which series to test: "innovations" (default) or "var"

Value

a data frame with columns variable, skewness, kurtosis, one p_<method> column per method, and all_non_gauss (TRUE when every run test rejects normality for that variable).

References

Analogous to the multi-test residual check (Gauss_Tests) in the VARLiNGAM R code of Moneta, A., Entner, D., Hoyer, P. O., & Coad, A. (2013), Oxford Bulletin of Economics and Statistics, 75(5), 705-730. https://sites.google.com/site/dorisentner/publications/VARLiNGAM

Examples

s <- generate_varlingam_sample(n = 1000, seed = 42)
m <- lingam_var(s$data, lags = 1, reg_method = "ols", prune = FALSE)
test_varlingam_residual_normality_all(m, methods = c("shapiro", "jb"))
#> Registered S3 method overwritten by 'quantmod':
#>   method            from
#>   as.zoo.data.frame zoo 
#>   variable     skewness  kurtosis    p_shapiro         p_jb all_non_gauss
#> 1       x0  0.088013433 -1.219504 6.041150e-18 1.898481e-14          TRUE
#> 2       x1 -0.008502543 -1.237896 3.173909e-17 1.398881e-14          TRUE
#> 3       x2 -0.045078629 -1.209086 4.530246e-17 5.162537e-14          TRUE