sm.autoregression: Nonparametric estimation of the autoregression function

In sm: Smoothing Methods for Nonparametric Regression and Density Estimation

Nonparametric estimation of the autoregression function

Description

This function estimates nonparametrically the autoregression function (conditional mean given the past values) of a time series x, assumed to be stationary.

Usage

sm.autoregression(x, h = hnorm(x), d = 1, maxlag = d, lags,
                  se = FALSE, ask = TRUE)

Arguments

x	vector containing the time series values.
h	the bandwidth used for kernel smoothing.
d	number of past observations used for conditioning; it must be 1 (default value) or 2.
maxlag	maximum of the lagged values to be considered (default value is d).
lags	if d==1, this is a vector containing the lags considered for conditioning; if d==2, this is a matrix with two columns, whose rows contains pair of values considered for conditioning.
se	if se==T, pointwise confidence bands are computed of approximate level 95%.
ask	if ask==TRUE, the program pauses after each plot until <Enter> is pressed.

Details

see Section 7.3 of the reference below.

Value

a list with the outcome of the final estimation (corresponding to the last value or pairs of values of lags), as returned by sm.regression.

Side Effects

graphical output is produced on the current device.

References

Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.

See Also

sm.regression, sm.ts.pdf

Examples

sm.autoregression(log(lynx), maxlag=3, se=TRUE)
sm.autoregression(log(lynx), lags=cbind(2:3,4:5))

sm documentation built on May 29, 2024, 2:28 a.m.