Re: Association between two variables over time

Posted by Jon Peck on Jun 16, 2021; 3:35pm
URL: http://spssx-discussion.165.s1.nabble.com/Association-between-two-variables-over-time-tp5740594p5740596.html

Tough problem.  Maybe one avenue to investigate is to disaggregate the series to a common base.  The STATS DISAGG extension command can do this.

From its help...

CONVERSIONMETHOD (optional) specifies the conversion algorithm. The Chow-Lin, Litterman, and Fernandez methods (with variations) use generalized least squares of the low frequency variable on the aggregated values of the high frequency series and then distribute the differences in the result across the quarterly values. The estimated relationship between the low frequency series (actual and aggregated) is assumed to hold also for the high frequency series. The GLS estimators include estimation of an autoregression parameter, rho. The OLS method does ordinary least squares (with no autocorrelation parameter). These methods all require at least one high frequency indicator.

The remaining methods (Denton type) can be used with or without an indicator variable and minimize the sum of squared deviations of the low and high frequency series using two additional parameters. Only one indicator variable is allowed.

For details on these algorithms, see http://journal.r-project.org/archive/2013-2/sax-steiner.pdf

On Wed, Jun 16, 2021 at 3:26 AM AndreasV <[hidden email]> wrote:

I would like to investigate the association between serial measurements of
two metric outcomes (e.g., two laboratory parameters measures repeatedly
over several years). Depending on the data set, measurements may have been
obtained at the same time points for all subjects (study data) or at
different time points ('real world' data). Moreover, I may or may not have
to perform comparisons between subsets of subjects (defined, e.g., by
different treatments received).

- What is the most appropriate statistical procedure for this, and
- (how) can this be done in SPSS (I use Version 24)?

Thank you very much!

Andreas



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