dccmidas_loglik {dccmidas} | R Documentation |
DCC-MIDAS log-likelihood (second step)
Description
Obtains the log-likelihood of the DCC models in the second step. For details, see Colacito et al. (2011) and Engle (2002).
Usage
dccmidas_loglik(param, res, lag_fun = "Beta", N_c, K_c)
Arguments
param |
Vector of starting values. |
res |
Array of standardized daily returns, coming from the first step estimation. |
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
The resulting vector is the log-likelihood value 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.