Description Usage Arguments Details Value Side Effects References See Also Examples

This function estimates nonparametrically the autoregression function
(conditional mean given the past values) of a time series `x`

,
assumed to be stationary.

1 2 | ```
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 produced 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.

1 2 | ```
sm.autoregression(log(lynx), maxlag=3, se=TRUE)
sm.autoregression(log(lynx), lags=cbind(2:3,4:5))
``` |

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