% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predict.R
\name{predict.dcvar_model_fit}
\alias{predict.dcvar_model_fit}
\alias{predict.dcvar_multilevel_fit}
\alias{predict.dcvar_multilevel_tv_fit}
\alias{predict.dcvar_sem_fit}
\alias{predict.dcvar_tv_fit}
\title{One-step-ahead predictions from a copula VAR model}
\usage{
\method{predict}{dcvar_model_fit}(object, type = c("link", "response"), ci_level = 0.95, ...)

\method{predict}{dcvar_multilevel_fit}(object, type = c("link", "response"), ci_level = 0.95, ...)

\method{predict}{dcvar_multilevel_tv_fit}(object, type = c("link", "response"), ci_level = 0.95, ...)

\method{predict}{dcvar_sem_fit}(object, type = c("link", "response"), ci_level = 0.95, ...)

\method{predict}{dcvar_tv_fit}(object, type = c("link", "response"), ci_level = 0.95, ...)
}
\arguments{
\item{object}{A fitted model object.}

\item{type}{Character; \code{"link"} (default) returns values on the model's
internal scale (standardized if applicable), \code{"response"} back-transforms
to the original data scale.}

\item{ci_level}{Prediction interval level (default: 0.95).}

\item{...}{Additional arguments (unused).}
}
\value{
A data frame of marginal prediction intervals at the specified
level. Single-level and SEM fits return columns \code{time}, \code{variable},
\code{mean}, \code{lower}, \code{upper}. Multilevel fits additionally include \code{unit}.
}
\description{
Returns point predictions and \strong{marginal} prediction intervals by combining
the VAR(1) fitted values with the estimated innovation SDs. Intervals are
computed per-variable using a normal approximation and do not account for
the copula dependence structure between variables. They are plug-in
intervals built from posterior-mean parameters: posterior parameter
uncertainty is not propagated, so coverage is understated on short series
where that uncertainty is non-negligible.
}
\details{
\code{predict()} is implemented for the public fit classes. For multilevel fits,
the methods return unit-specific trajectories. For SEM fits, \code{type = "link"}
returns latent states and \code{type = "response"} returns observed indicator
predictions.
}
