% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/estim.R
\name{svpSE}
\alias{svpSE}
\title{Simulation-Based Standard Errors for SV(p) Models}
\usage{
svpSE(object, n_sim = 199, alpha = 0.05, burnin = 500, logNu = FALSE)
}
\arguments{
\item{object}{A fitted model object from \code{\link{svp}}. Can be of class
\code{"svp"}, \code{"svp_t"}, or \code{"svp_ged"}.}

\item{n_sim}{Integer. Number of Monte Carlo replications. Default 199.}

\item{alpha}{Numeric. Significance level for confidence intervals. Default 0.05.}

\item{burnin}{Integer. Burn-in period for simulation. Default 500.}

\item{logNu}{Logical. Solve for \eqn{\nu} in log-space for numerical
stability (Student-t only). Default is \code{FALSE}.}
}
\value{
A list with:
\describe{
  \item{CI}{2 x k matrix of confidence intervals (lower, upper).}
  \item{SEsim0}{Standard errors relative to true parameter values.}
  \item{SEsim}{Standard errors relative to sample mean.}
  \item{ISEconservative}{Conservative interval-based standard errors.}
  \item{ISEliberal}{Liberal interval-based standard errors.}
  \item{thetamat}{Matrix of parameter estimates from simulations.}
}
}
\description{
Computes standard errors and confidence intervals for estimated parameters
by simulating from the fitted model and re-estimating. Supports all model
types returned by \code{\link{svp}}: Gaussian (with or without leverage),
Student-t, and GED.
}
\examples{
\donttest{
# Gaussian SV(1)
y <- sim_svp(1000, phi = 0.95, sigy = 1, sigv = 0.2)$y
fit <- svp(y)
se <- svpSE(fit, n_sim = 49)
se$CI
}

}
