# sm.regression.autocor: Nonparametric regression with autocorrelated errors In sm: Smoothing Methods for Nonparametric Regression and Density Estimation

## Description

This function estimates nonparametrically the regression function of `y` on `x` when the error terms are serially correlated.

## Usage

 `1` ```sm.regression.autocor(x = 1:n, y, h.first, minh, maxh, method = "direct", ...) ```

## Arguments

 `y` vector of the response values `h.first` the smoothing parameter used for the initial smoothing stage. `x` vector of the covariate values; if unset, it is assumed to be `1:length(y)`. `minh` the minimum value of the interval where the optimal smoothing parameter is searched for (default is 0.5). `maxh` the maximum value of the interval where the optimal smoothing parameter is searched for (default is 10). `method` character value which specifies the optimality criterium adopted; possible values are `"no.cor"`, `"direct"` (default), and `"indirect"`. `...` other optional parameters are passed to the `sm.options` function, through a mechanism which limits their effect only to this call of the function. Those relevant for this function are the following: `ngrid`, `display`; see the documentation of `sm.options` for their description.

## Details

see Section 7.5 of the reference below.

## Value

a list as returned from sm.regression called with the new value of smoothing parameter, with an additional term `\$aux` added which contains the initial value `h.first`, the estimated curve using `h.first`, the autocorrelation function of the residuals from the initial fit, and the residuals.

## Side Effects

a new suggested value for `h` is printed; also, if the parameter `display` is not equal to `"none"`, graphical output is produced on the current graphical 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.

`sm.regression`, `sm.autoregression`