Note: This page has been translated by MathWorks. Click here to see

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Sample partial autocorrelation

`parcorr(y)`

`parcorr(y,Name,Value)`

`pacf = parcorr(___)`

```
[pacf,lags,bounds]
= parcorr(___)
```

`parcorr(ax,___)`

```
[pacf,lags,bounds,h]
= parcorr(___)
```

`parcorr(`

plots the sample
partial autocorrelation function (PACF) of the univariate,
stochastic time series `y`

)`y`

with confidence bounds.

`parcorr(`

uses
additional options specified by one or more name-value pair arguments. For
example, `y`

,`Name,Value`

)`parcorr(y,'NumLags',10,'NumSTD',2)`

plots the sample
PACF of `y`

for `10`

lags and displays
confidence bounds consisting of `2`

standard errors.

returns the sample
PACF of `pacf`

= parcorr(___)`y`

using any of the input arguments in the previous
syntaxes.

`parcorr(`

plots on the axes specified by `ax`

,___)`ax`

instead
of the current axes (`gca`

). `ax`

can precede any of the input
argument combinations in the previous syntaxes.

To plot the PACF without confidence bounds, set `'NumSTD',0`

.

`parcorr`

plots the PACF when you do not request any output or
when you request the fourth output.

[1] Box, G. E. P., G. M. Jenkins, and G. C.
Reinsel. *Time Series Analysis: Forecasting and Control*.
3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1994.

[2] Hamilton, J. D. *Time Series Analysis*.
Princeton, NJ: Princeton University Press, 1994.