Partial Autocorrelation Function
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Stat > Time Series > Partial Autocorrelation

Partial autocorrelation computes and plots the partial autocorrelations of a time series. Partial autocorrelations, like autocorrelations, are correlations between sets of ordered data pairs of a time series. As with partial correlations in the regression case, partial autocorrelations measure the strength of relationship with other terms being accounted for. The partial autocorrelation at a lag of k is the correlation between residuals at time t from an autoregressive model and observations at lag k with terms for all intervening lags present in the autoregressive model. The plot of partial autocorrelations is called the partial autocorrelation function or PACF. View the PACF to guide your choice of terms to include in an ARIMA model. See Fitting an ARIMA model.

Dialog box items

Series: Choose the column containing the time series.

Default number of lags: Choose to use the default number of lags. This is n / 4 for a series with less than or equal to 240 observations or + 45 for a series with more than 240 observations, where n is the number of observations in the series.

Number of lags: Choose to enter the number of lags to use instead of the default. The maximum number of lags is equal to n - 1.

Store PACF: Check to store the partial autocorrelations in the next available column.

Store t statistics: Check to store the t-statistics.

Title: Enter a new title to replace the default title on the graphical output.