gmvarkit (version 1.4.1)

add_data: Add data to an object of class 'gmvar' defining a GMVAR model

Description

add_data adds or updates data to object of class 'gmvar' that defines a GMVAR model. Also calculates mixing weights and quantile residuals accordingly.

Usage

add_data(data, gmvar, calc_cond_moments = TRUE, calc_std_errors = FALSE)

Arguments

data

a matrix or class 'ts' object with d>1 columns. Each column is taken to represent a single time series. NA values are not supported.

gmvar

an object of class 'gmvar' created with fitGMVAR or GMVAR.

calc_cond_moments

should conditional means and covariance matrices should be calculated? Default is TRUE if the model contains data and FALSE otherwise.

calc_std_errors

should approximate standard errors be calculated?

Value

Returns an object of class 'gmvar' defining the specified GMVAR model with the data added to the model. If the object already contained data, the data will be updated.

References

• Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.

• Virolainen S. 2020. Structural Gaussian mixture vector autoregressive model. Unpublished working paper, available as arXiv:2007.04713.

See Also

fitGMVAR, GMVAR, iterate_more, update_numtols

Examples

# NOT RUN {
# These examples use the data 'eurusd' which comes with the
# package, but in a scaled form.
data <- cbind(10*eurusd[,1], 100*eurusd[,2])
colnames(data) <- colnames(eurusd)

# GMVAR(1,2), d=2 model:
params122 <- c(0.623, -0.129, 0.959, 0.089, -0.006, 1.006, 1.746,
 0.804, 5.804, 3.245, 7.913, 0.952, -0.037, -0.019, 0.943, 6.926,
 3.982, 12.135, 0.789)
mod122 <- GMVAR(p=1, M=2, d=2, params=params122)
mod122

mod122_2 <- add_data(data, mod122)
mod122_2


# GMVAR(2,2), d=2 model with AR-parameters restricted to be
# the same for both regimes:
C_mat <- rbind(diag(2*2^2), diag(2*2^2))
params222c <- c(1.031, 2.356, 1.786, 3.000, 1.250, 0.060, 0.036,
 1.335, -0.290, -0.083, -0.047, -0.356, 0.934, -0.152, 5.201, 5.883,
 3.560, 9.799, 0.368)
mod222c <- GMVAR(p=2, M=2, d=2, params=params222c, constraints=C_mat)
mod222c

mod222c_2 <- add_data(data, mod222c)
mod222c_2

# Structural GMVAR(2, 2), d=2 model identified with sign-constraints:
params222s <- c(1.03, 2.36, 1.79, 3, 1.25, 0.06, 0.04, 1.34, -0.29,
 -0.08, -0.05, -0.36, 1.2, 0.05, 0.05, 1.3, -0.3, -0.1, -0.05, -0.4,
  0.89, 0.72, -0.37, 2.16, 7.16, 1.3, 0.37)
W_222 <- matrix(c(1, 1, -1, 1), nrow=2, byrow=FALSE)
mod222s <- GMVAR(p=2, M=2, d=2, params=params222s, structural_pars=list(W=W_222))
mod222s

mod222s_2 <- add_data(data, mod222s)
mod222s_2
# }