Time Series Analysis - Partial Autocorrelations

A partial autocorrelation is the correlation of a series with itself, shifted by a particular lag of k observations, and controlling for the correlations for all shifts of 1 through k-1. The plot of partial autocorrelations for various lags is a crucial tool for determining an appropriate model for ARIMA analysis (see ARIMA Overview and Time Series Analysis Index). The computations of the partial autocorrelation coefficients φ_k follow the standard formulas, as described in most time series references (e.g., Box & Jenkins, 1976).

Standard error of φk. Under the assumption that the true autoregressive process in the series is of order p ≤ k-1, then the approximate standard error of φ_k is defined as:

StdErr(φ_k) = Ö(1/N)

Here N is the number of observations in the series.

Copyright © 2021. Cloud Software Group, Inc. All Rights Reserved.