R: Nonparametric density estimation of stationary time series...

sm.ts.pdf {sm} | R Documentation

## Nonparametric density estimation of stationary time series data

### Description

This function estimates the density function of a time series `x`, assumed to be stationary. The univariate marginal density is estimated in all cases; bivariate densities of pairs of lagged values are estimated depending on the parameter `lags`.

### Usage

    sm.ts.pdf(x, h = hnorm(x), lags, maxlag = 1, ask = TRUE)

### Arguments

`x` | a vector containing a time series
`h` | bandwidth
`lags` | for each value, `k` say, in the vector `lags` a density estimate is produced of the joint distribution of the pair `(x(t-k),x(t))`.
`maxlag` | if `lags` is not given, it is assigned the value `1:maxlag` (default=1).
`ask` | if `ask=TRUE`, the program pauses after each plot, until <Enter> is pressed.

### Details

see Section 7.2 of the reference below.

### Value

a list of two elements, containing the outcome of the estimation of the marginal density and the last bivariate density, as produced by `sm.density`.

### Side Effects

plots are 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.density`, `sm.autoregression`

### Examples

    with(geyser, {
       sm.ts.pdf(geyser$duration, lags=1:2)
    })

[Package sm version 2.2-5.4 Index]