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.


[Package dccmidas version 0.1.2 Index]