unit -invariant variable

[aroopsind]

Hi,

I am building out a model where one of my covariates, elevation, is fixed and does not vary with time as the outcome and some other covariates (e.g. pop?).

I created a long structure data format and for the column with the elevation value it repeats for each year there is an observation. I, however, get the following error: "Error in gsynth.default(formula = NULL, data = data, Y = Yname, D = Dname, : Variable "elevation" is unit-invariant. Try to remove it. Timing stopped at: 10.85 0.32 13.47"

Any guidance on the best way to include these time-invariate covariates would be greatly appreciated.

Anand

[xuyiqing]

Hi Anand,

Like in fixed effects, gsynth does not take in time-invariant covariates because they have already been "control for" with the additive and interactive fixed effects.

Best, Yiqing

On Tue, Mar 24, 2020 at 9:38 AM Anand Roopsind notifications@github.com wrote:

Hi,

I am building out a model where one of my covariates, elevation, is fixed and does not vary with time as the outcome and some other covariates (e.g. pop?).

I created a long structure data format and for the column with the elevation value it repeats for each year there is an observation. I, however, get the following error: "Error in gsynth.default(formula = NULL, data = data, Y = Yname, D = Dname, : Variable "elevation" is unit-invariant. Try to remove it. Timing stopped at: 10.85 0.32 13.47"

Any guidance on the best way to include these time-invariate covariates would be greatly appreciated.

Anand

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-- Yiqing Xu

Assistant Professor Department of Political Science Stanford University http://yiqingxu.org/

[aroopsind]

Thanks for that clarification Yiping. I went back through the gsynth publication and supplemental - is there an example of the use of the additive and interactive fixed effects model to control for these time-invariant covariates and then the application of gsynth.

In one analysis I am exploring with gsynth, as a first step I used propensity score matching to identify the pool of control units based on time-invariant covariates. Curious how different these would be based on the IFE. Thanks again for taking the time to respond.

Cheers Anand

[xuyiqing]

Trimming based on time-invariant covariates before running gsynth/IFE is certainly allowed and may improve overlap and speed. I think it's a good idea if you have a lot of data.

On Sun, Apr 5, 2020 at 10:05 AM Anand Roopsind notifications@github.com wrote:

Thanks for that clarification Yiping. I went back through the gsynth publication and supplemental - is there an example of the use of the additive and interactive fixed effects model to control for these time-invariant covariates and then the application of gsynth.

In one analysis I am exploring with gsynth, as a first step I used propensity score matching to identify the pool of control units based on time-invariant covariates. Curious how different these would be based on the IFE. Thanks again for taking the time to respond.

Cheers Anand

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-- Yiqing Xu

Assistant Professor Department of Political Science Stanford University http://yiqingxu.org/

[jtorcasso]

Hello, I was wondering if the package allowed for including observed time-invariant covariates, as discussed in Remark 3 of the paper.

[xuyiqing]

They are not compatible with gsynth, however, another package we worked on, bpCausal, can incorporate them.

On Thu, Dec 16, 2021 at 10:36 AM jtorcasso @.***> wrote:

Hello, I was wondering if the package allowed for including observed time-invariant covariates, as discussed in Remark 3 of the paper.

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-- Yiqing Xu

Assistant Professor Department of Political Science Stanford University https://yiqingxu.org/

[jtorcasso]

Thanks! Will check it out.