ARIMA Time Series Modelling Of Excess Mortality 1975 – 2021: outlier method

England & Wales monthly excess mortality by quinary age band & sex (rev 1.1)

In a recent newsletter I undertook statistical modelling of this dataset and opted to share the sizeable output file with subscribers instead of pasting 36 slides and copious tabulations in a series of lengthy posts. I haven’t had any feedback from subscribers as yet but I thought I’d bravely plough on and try this method of dissemination once again for a differing methodology.

Outlying Months

The idea this time round was to junk my raft of 13 monthly indicator variables and use the ARIMA time series procedure itself to flag up months between Jan 1975 and Dec 2021 where excess mortality was found to differ markedly from the 5-year baseline ‘norm’.

Source Files

Paying subscribers wishing to take a peek at these results should ideally start by reading this newsletter first, followed by this newsletter. The file you require once again is EW DOD excess monthly mortality 1975 – 2021 (ARIMA).spv, which may be found sitting in a shared Google Drive folder which may be accessed here.

What Constitutes An Outlier?

A darn good question. The ARIMA forecasting module within SPSS defines no less than 7 different types:

The fully automated expert modeller was utilised as before to ensure consistency in model identification. In practice I am usually able to develop a superior model manually but this takes time and patience and the automated models always pass muster. Given that I was facing the tasking of identifying and developing 36 ARIMA models with little spare time this morning it was pretty much a no-brainer to press the big button and sit back!