skew_t_fun: Skewed t distribution

In ftsa: Functional Time Series Analysis

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 n by p
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)

ftsa documentation built on Sept. 11, 2023, 5:09 p.m.