The ARIMA Procedure

Missing Values and Autocorrelations

To compute the sample autocorrelation function when missing values are present, PROC ARIMA uses only crossproducts that do not involve missing values and employs divisors that reflect the number of crossproducts used rather than the total length of the series. Sample partial autocorrelations and inverse autocorrelations are then computed by using the sample autocorrelation function. If necessary, a taper is employed to transform the sample autocorrelations into a positive definite sequence before calculating the partial autocorrelation and inverse correlation functions. The confidence intervals produced for these functions might not be valid when there are missing values. The distributional properties for sample correlation functions are not clear for finite samples. See Dunsmuir (1984) for some asymptotic properties of the sample correlation functions.