skew_t_fun {ftsa} | R Documentation |
Skewed t distribution
Description
Fitting a parametric skewed t distribution of Fernandez and Steel's (1998) method
Usage
skew_t_fun(data, gridpoints, M = 5001)
Arguments
data |
a data matrix of dimension |
gridpoints |
Grid points |
M |
number of grid points |
Details
1) Fit a skewed t distribution to data, and obtain four latent parameters; 2) Transform the four latent parameters so that they are un-constrained; 3) Fit a vector autoregressive model to these transformed latent parameters; 4) Obtain their forecasts, and then back-transform them to the original scales; 5) Via the skewed t distribution in Step 1), we obtain forecast density using the forecast latent parameters.
Value
m |
Grid points within data range |
skewed_t_den_fore |
Density forecasts via a skewed t distribution |
Note
This is a parametric approach for fitting and forecasting density.
Author(s)
Han Lin Shang
References
Fernandez, C. and Steel, M. F. J. (1998), ‘On Bayesian modeling of fat tails and skewness’, Journal of the American Statistical Association: Theory and Methods, 93(441), 359-371.
See Also
CoDa_FPCA
, Horta_Ziegelmann_FPCA
, LQDT_FPCA
Examples
skew_t_fun(DJI_return)