I wasn't able to find any information online about using distributed lag models in SAS. This is different from what PROC PDLREG does and is used extensively to to model ambient temperature and pollution data. I was wondering if SAS has any plans to introduce a procedure for this.
[EDIT : Have moved this to "SAS Econometrics and Forecasting" - board by the way ]
DLNM = Distributed lag non-linear models
(it's like PROC PDLREG , but allowing for more flexibility in the relationship between regressors and target time series)
DLNM is a modeling framework to flexibly describe associations showing potentially non-linear and delayed effects in time series data, thus a framework for estimating dynamic relationships.
Just note that this can be done in several other ways within SAS !!
Thanks for the response Koen. Could you elaborate the ways one can do similar analysis in SAS? How does one go about creating the crossbasis functions that can incorporate the temperature along the non-linear and lagged dimension ? I need to do conditional logit on individual level data. It is a case crossover design with 1:5 matching.
There is some support for identifying delayed effects in HPFDIAGNOSE (SAS Foundation) and the DIAGNOSE Object of the ATSM Package (SAS Viya). Look for DELAYINPUT and DELAYEVENT.
'DELAYEVENT' takes a nonnegative numeric Value that specifies the event variable lag.
'DELAYINPUT' takes a nonnegative numeric Value that specifies the input variable lag. If not specified,
the delay lags for the inputs are automatically chosen.
Another option would be to call R from SAS. However, if there is support for this method in Python, that would be easier to integrate with SAS.
Here is a blog that shows using Python from SAS.
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