qser2sar: Spline Autoregression (SAR) Model of Quantile Series

In qfa: Quantile-Frequency Analysis (QFA) of Time Series

Spline Autoregression (SAR) Model of Quantile Series

Description

This function fits spline autoregression (SAR) model to quantile series (QSER).

Usage

qser2sar(
  y.qser,
  tau0,
  tau = tau0,
  p = NULL,
  order.max = NULL,
  spar = NULL,
  method = c("GCV", "AIC", "BIC"),
  weights = rep(1, length(tau0)),
  interval = c(-1.5, 1.5)
)

Arguments

y.qser	matrix or array of pre-calculated QSER at tau0 using, e.g., qser()
tau0	quantile levels used to compute y.qser
tau	quantile levels for evaluation (min(tau0) <= tau <= max(tau0); default = tau0)
p	order of SAR model (default = NULL: automatically selected by AIC)
order.max	maximum order for AIC if p = NULL (default = NULL: determined by stats::ar())
spar	penalty parameter alla stats::smooth.spline() (default = NULL: automatically selected)
method	criterion for penalty parameter selection: "GCV" (default), "AIC", or "BIC"
weights	sequence of weights (default = rep(1,length(tau0)))
interval	interval for spar optimization (default = c(-1.5,1.5))

Value

a list with the following elements:

A	matrix or array of SAR coefficients
V	vector or matrix of SAR residual covariance
p	order of SAR model
spar	penalty parameter
n	length of time series
tau	quantile levels for evaluation
tau0	quantile levels for fitting
weights	weights in penalty function
fit	object containing details of SAR fit

qfa documentation built on Sept. 11, 2025, 9:09 a.m.