dccmidas_mat_est {dccmidas} | R Documentation |
Obtains the matrix H_t, R_t and long-run correlations, under the DCC-MIDAS model
Description
Obtains the matrix H_t, R_t and long-run correlations, under the DCC-MIDAS model For details, see Colacito et al. (2011) and Engle (2002).
Usage
dccmidas_mat_est(est_param, res, Dt, lag_fun = "Beta", N_c, K_c)
Arguments
est_param |
Vector of estimated values |
res |
Array of standardized daily returns, coming from the first step estimation |
Dt |
Matrix of conditional standard deviations (coming from the first step) |
lag_fun |
optional. Lag function to use. Valid choices are "Beta" (by default) and "Almon", for the Beta and Exponential Almon lag functions, respectively |
N_c |
Number of (lagged) realizations to use for the standarized residuals forming the long-run correlation |
K_c |
Number of (lagged) realizations to use for the long-run correlation |
Value
A list with the H_t
, R_t
and long-run correlaton matrices, for each t
.
References
Colacito R, Engle RF, Ghysels E (2011).
“A component model for dynamic correlations.”
Journal of Econometrics, 164(1), 45–59.
doi:10.1016/j.jeconom.2011.02.013.
Engle R (2002).
“Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models.”
Journal of Business & Economic Statistics, 20(3), 339–350.
doi:10.1198/073500102288618487.