astMLE | R Documentation |
Method for fitting an AST distribution to a univariate data series by Maximum Likelihood Estimation,
returns an ast
object.
astMLE(
data,
start_pars = c(),
fixed_pars = c(),
solver = c("nlminb", "nloptr", "Rsolnp"),
solver_control = list(),
symmetric = FALSE
)
data |
a univariate data object to be fitted |
start_pars |
a named numeric vector of starting parameters for the optimization algorithm, not all parameters are needed |
fixed_pars |
a named numeric vector of parameters to be kept fixed during the optimization routine, not all parameters are needed |
solver |
solver used for MLE, one of 'nlminb', 'nloptr', 'Rsolnp', default is 'nlminb' |
solver_control |
list of control arguments passed to the solver |
symmetric |
a logical argument, when TRUE, the function fits an SST distribution(Symmetric Student-t, nu1 = nu2) instead, default to FALSE |
The astMLE
function fits an AST distribution to a univariate data series by estimating the distribution parameters
through Maximum Likelihood Estimation.
For details of the list of control arguments, please refer to nlminb
, nloptr::nloptr
, Rsolnp::solnp
A ast
object(S3), the components of the object are:
data |
the univariate data object for the AST distribution to be fitted |
solver |
the solver called |
solver_control |
the list of control argumetns passed to the solver called |
start_pars |
named numeric vector of starting parameters used |
fixed_pars |
named numeric vector of fixed parameters used |
symmetric |
logical argument controlling the symmetry of tail parameters in the MLE |
solver_result |
output of the called solver |
fitted_pars |
named vector of fitted arguemnts of the AST distribution |
objective |
the optimal log-likelihood value obtained by the solver |
time_elapsed |
the time elapesed for the MLE routine |
message |
the message of convergence status produced by the called solver |
standard_errors |
standard errors of the fitted parameters |
Zhu, D., & Galbraith, J. W. (2010). A generalized asymmetric Student-t distribution with application to financial econometrics. Journal of Econometrics, 157(2), 297-305. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1016/j.jeconom.2010.01.013")}
pars <- c(0.12, 0.6, 0.6, 3, 5)
data <- rast(1000, pars = pars)
solver_control <- list(eval.max = 10^3, iter.max = 10^3)
fit <- astMLE(data, solver_control = solver_control)
fit
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