Last chance! 50% off unlimited learning
Sale ends in
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)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.
vector containing the time series values.
the bandwidth used for kernel smoothing.
number of past observations used for conditioning; it must be 1 (default value) or 2.
maximum of the lagged values to be considered (default value is d).
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.
if se==T, pointwise confidence bands are computed of approximate level 95%.
if ask==TRUE, the program pauses after each plot until <Enter> is pressed.
graphical output is produced on the current device.
see Section 7.3 of the reference below.
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.regression, sm.ts.pdf
sm.autoregression(log(lynx), maxlag=3, se=TRUE)
sm.autoregression(log(lynx), lags=cbind(2:3,4:5))
Run the code above in your browser using DataLab