qser2ar: Autoregression (AR) Model of Quantile Series

In qfa: Quantile-Frequency Analysis (QFA) of Time Series and Spline Quantile Regression (SQR)

Autoregression (AR) Model of Quantile Series

Description

This function fits an autoregression (AR) model to quantile series (QSER) separately for each quantile level using stats::ar().

Usage

qser2ar(
  y.qser,
  p = NULL,
  order.max = NULL,
  method = c("none", "gamm", "sp"),
  spar = NULL,
  type = c(1, 2)
)

Arguments

y.qser	matrix or array of pre-calculated QSER, e.g., using qser()
p	order of AR model (default = NULL: selected by AIC)
order.max	maximum order for AIC if p = NULL (default = NULL: determined by stats::ar())
method	quantile smoothing method: "gamm" for mgcv::gamm(), "sp" for stats::smooth.spline(), or "none" (default)
spar	smoothing parameter for stats::smooth.spline() (default = NULL for GCV)
type	type of smoothing for covariance matrix: 1 (direct, default) or 2 (square root)

Value

a list with the following elements:

A	matrix or array of AR coefficients
V	vector or matrix of residual covariance
p	order of AR model
n	length of time series
residuals	matrix or array of residuals

qfa documentation built on March 30, 2026, 5:07 p.m.