Return Standardized Estimates to the Original Scale
Arguments
- coef_std
Numeric matrix. Standardized estimates of the autoregression and cross regression coefficients.
- Y
Numeric matrix. Matrix of dependent variables (Y).
- X
Numeric matrix. Matrix of predictors (X).
See also
Other Fitting Autoregressive Model Functions:
FitMLVARDynr(),
FitMLVARMplus(),
FitVARDynr(),
FitVARLassoSearch(),
FitVARLasso(),
FitVARMplus(),
FitVAROLS(),
LambdaSeq(),
ModelVARP1Dynr(),
ModelVARP2Dynr(),
PBootVARExoLasso(),
PBootVARExoOLS(),
PBootVARLasso(),
PBootVAROLS(),
RBootVARExoLasso(),
RBootVARExoOLS(),
RBootVARLasso(),
RBootVAROLS(),
SearchVARLasso(),
StdMat()
Examples
Y <- dat_p2_yx$Y
X <- dat_p2_yx$X[, -1] # remove the constant column
YStd <- StdMat(Y)
XStd <- StdMat(X)
coef_std <- FitVAROLS(Y = YStd, X = XStd)
FitVAROLS(Y = Y, X = X)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0.387920808 0.03231299 0.04869624 0.136574680 0.04612981 0.01316764
#> [2,] 0.038101548 0.51204958 0.01610152 -0.002875789 0.26175258 0.04930620
#> [3,] -0.003827389 0.04704638 0.64303935 0.015204624 0.02304969 0.32942855
OrigScale(coef_std = coef_std, Y = Y, X = X)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0.36836768 0.008539283 0.02231498 0.11338780 0.024505442 -0.01143384
#> [2,] 0.01334496 0.481949237 -0.01730029 -0.03223315 0.234373552 0.01815779
#> [3,] -0.03022951 0.014945306 0.60741736 -0.01610408 -0.006149189 0.29620975