R: Nonparametric estimation of the autoregression function

sm.autoregression {sm} | R Documentation

## 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 producved 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))

[Package sm version 2.2-5.4 Index]