# Nonparametric regression with autocorrelated errors

### 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 |

`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 |

`...` |
other optional parameters are passed to the |

### 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.

### See Also

`sm.regression`

, `sm.autoregression`