tsqr.fit {qfa}

Trigonometric Spline Quantile Regression (TSQR)

Description

This function computes trigonometric spline quantile regression (TSQR) for univariate time series at a single frequency.

Usage

tsqr.fit(y, f0, tau, c0, d = 1, weighted = FALSE, prepared = TRUE)

Arguments

y

vector of time series

f0

frequency in [0,1)

tau

sequence of quantile levels in (0,1)

c0

penalty parameter

d

subsampling rate of quantile levels (default = 1)

weighted

if TRUE, penalty function is weighted (default = FALSE)

prepared

if TRUE, intercept is removed and coef of cosine is doubled when f0 = 0.5

Value

object of sqr.fit() (coefficients in $coef)

Examples

y <- stats::arima.sim(list(order=c(1,0,0), ar=0.5), n=64)
tau <- seq(0.1,0.9,0.05)
fit <- tqr.fit(y,f0=0.1,tau=tau)
fit.sqr <- tsqr.fit(y,f0=0.1,tau=tau,c0=0.02,d=4)
plot(tau,fit$coef[1,],type='p',xlab='QUANTILE LEVEL',ylab='TQR COEF')
lines(tau,fit.sqr$coef[1,],type='l')