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Create Y and X Matrices

Source: R/RcppExports.R
YX.Rd

This function creates the dependent variable (Y) and predictor variable (X) matrices.

Usage

YX(data, p)

Arguments

data

Numeric matrix. The time series data with dimensions t by k, where t is the number of observations and k is the number of variables.

p

Integer. The order of the VAR model (number of lags).

Value

List containing the dependent variable (Y) and predictor variable (X) matrices. Note that the resulting matrices will have t - p rows.

Details

The YX() function creates the Y and X matrices required for fitting a Vector Autoregressive (VAR) model. Given the input data matrix with dimensions t by k, where t is the number of observations and k is the number of variables, and the order of the VAR model p (number of lags), the function constructs lagged predictor matrix X and the dependent variable matrix Y.

The steps involved in creating the Y and X matrices are as follows:

  • Determine the number of observations t and the number of variables k from the input data matrix.

  • Create matrices X and Y to store lagged variables and the dependent variable, respectively.

  • Populate the matrices X and Y with the appropriate lagged data. The predictors matrix X contains a column of ones and the lagged values of the dependent variables, while the dependent variable matrix Y contains the original values of the dependent variables.

  • The function returns a list containing the Y and X matrices, which can be used for further analysis and estimation of the VAR model parameters.

See also

The SimVAR() function for simulating time series data from a VAR model.

Other Simulation of Autoregressive Data Functions: BootCI(), BootSE(), SelectVARLasso(), SimVARExo(), SimVAR(), YXExo()

Author

Ivan Jacob Agaloos Pesigan

Examples

set.seed(42)
time <- 50L
burn_in <- 10L
k <- 3
p <- 2
constant <- c(1, 1, 1)
coef <- matrix(
  data = c(
    0.4, 0.0, 0.0, 0.1, 0.0, 0.0,
    0.0, 0.5, 0.0, 0.0, 0.2, 0.0,
    0.0, 0.0, 0.6, 0.0, 0.0, 0.3
  ),
  nrow = k,
  byrow = TRUE
)
chol_cov <- chol(diag(3))
y <- SimVAR(
  time = time,
  burn_in = burn_in,
  constant = constant,
  coef = coef,
  chol_cov = chol_cov
)
yx <- YX(data = y, p = 2)
str(yx)
#> List of 2
#>  $ X: num [1:48, 1:7] 1 1 1 1 1 1 1 1 1 1 ...
#>  $ Y: num [1:48, 1:3] 1.8 3.28 1.95 0.61 1.44 ...