sm.autoregression {sm} | R Documentation |
This function estimates nonparametrically the autoregression function
(conditional mean given the past values) of a time series x
,
assumed to be stationary.
sm.autoregression(x, h = hnorm(x), d = 1, maxlag = d, lags, se = FALSE, ask = TRUE)
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 |
lags |
if |
se |
if |
ask |
if |
see Section 7.3 of the reference below.
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
.
graphical output is producved on the current device.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
sm.autoregression(log(lynx), maxlag=3, se=TRUE) sm.autoregression(log(lynx), lags=cbind(2:3,4:5))