ChengTS: Fit a nonlinear AR1 model using local polynomial regression...

In RCMinification: Random Coefficient Minification Time Series Models

Fit a nonlinear AR1 model using local polynomial regression via the method of Cheng et al.

Description

This function uses local polynomial regression to nonparametrically estimate the autoregression function in a nonlinear AR1 model using Cheng's bias reduction method.

Usage

ChengTS(z, degree = 1, hopt, ...)

Arguments

z	numeric vector of time series observations.
degree	numeric, degree of local polynomial fit.
hopt	numeric, base bandwidth for local polynomial estimate.
...	any other arguments taken by locpoly.

Value

A list containing

x	numeric vector of evaluation points.
y	numeric vector of nonparametric estimates at the values in x.

Author(s)

L. Han and S. Snyman

References

Cheng, M., Huang, R., Liu, P. and Liu, H. (2018) Bias reduction for nonparametric and semiparametric regression models. Statistica Sinica 28(4):2749-2770.

Examples

x <- nonlinearAR1.sim(100, g = function(x) x*sin(x),  sd = 1.5) # simulated data 
ChengTS(x, degree = 1, hopt = 0.5)

x <- nonlinearAR1.sim(100, g = function(x) x*sin(x),  sd = 0.5) # simulated data 
degree <- 1; xrange <- c(-.5, .5); n <- length(x)
h <- thumbBw(x[-n], x[-1], deg = degree, kernel=gaussK)
x.lp <- ARlocpoly(x, deg = degree, h = h, range.x = xrange)
x.shp <- sharpARlocpoly(x, deg = degree, range.x = xrange, h = x.lp$h*n^(4/45))
x.cheng <- ChengTS(x, degree = degree,  hopt = h, range.x = xrange)
lag.plot(x, do.lines=FALSE)
lines(x.lp)
lines(x.shp, col=2)
lines(x.cheng, col=4)

RCMinification documentation built on Jan. 17, 2023, 5:14 p.m.