% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pmm2_inference.R
\name{ts_pmm2_inference}
\alias{ts_pmm2_inference}
\title{Bootstrap inference for PMM2 time series models}
\usage{
ts_pmm2_inference(
  object,
  x = NULL,
  B = 200,
  seed = NULL,
  block_length = NULL,
  method = c("residual", "block"),
  parallel = FALSE,
  cores = NULL,
  debug = FALSE,
  ci_method = c("normal", "percentile", "basic")
)
}
\arguments{
\item{object}{object of class TS2fit}

\item{x}{(optional) original time series; if NULL, uses \code{object@original_series}}

\item{B}{number of bootstrap replications}

\item{seed}{(optional) for reproducibility}

\item{block_length}{block length for block bootstrap; if NULL, uses heuristic value}

\item{method}{bootstrap type: "residual" or "block"}

\item{parallel}{logical, whether to use parallel computing}

\item{cores}{number of cores for parallel computing}

\item{debug}{logical, whether to output additional diagnostic information}

\item{ci_method}{character: confidence-interval method. See
\code{\link{pmm2_inference}} for the full description. The
default \code{"normal"} returns a Wald interval centred on the
point estimate, which is robust to the moderate downward bias
that block bootstrap induces for AR coefficients of strongly
persistent series. Use \code{"percentile"} for Efron's classical
percentile interval or \code{"basic"} for the Davison & Hinkley
(1997) pivotal interval.}
}
\value{
data.frame with columns: Estimate, Std.Error, bias,
t.value, p.value, conf.low, conf.high.
}
\description{
Residual- or block-bootstrap standard errors and confidence
intervals for PMM2 time-series fits. Block bootstrap can exhibit
substantial finite-sample bias for AR coefficients near the
stationarity boundary; see \code{ci_method} and the returned
\code{bias} column for diagnostics.
}
