Difference-in-differences using two time series
Difference-in-differences using two time series
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Stafanko · External communityPost link
External question — Cross Validated Stack Exchange
Author: Stafanko
Original post: https://stats.stackexchange.com/questions/381290
License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/
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I hope the group will be able to help on the following. I have the following policy-evaluation problem: stock exchange A reduced their trading costs after period X (say, after 2008), thus managing to spur their trading activity. Estimation of the actual effect is however impossible because of the lack of a counterfactual. Neighbouring exchange B, with similar characteristics and a similar trend in trading activity, does not apply any such or other confounding policies after the treatment period, thus representing a natural candidate as a control group.
While this setup seems perfect for a difference-in-differences estimation, I am left with an excruciating doubt: in both the treatment and the control groups I have a sample of 1. Can one use the argument that if the series are stationary and ergodic, one can estimate a Conditional Average Treatment Effect on the Treated (that is, a treatment effect on the sample of the treated, in this case exchange A?).
I haven't found similar applications, and perhaps for a reason...
Waiting for your expert comments!
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RegressForward · External communityPost link
External answer — Cross Validated Stack Exchange
Author: RegressForward
Original post: https://stats.stackexchange.com/a/381300
License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/
Adaptation: HTML converted to plain text; contact email addresses removed.
If you have a single treatment group
$(i=1)$
and a single control group
$(i=2)$
for many time periods before and after treatment it is perfectly acceptable to use a diff-in-diff methodology. (So on net, you'd need:
$T >= 2, I >=2, n >=4$
)
Additional control groups will help with the robustness and persuasiveness but are not required.
However, if you have only a single sample (n=1), then you cannot reasonably do a diff-in-diff examination, and I think that should be very clear.
Plug: You may want to consider the Economics StackExchange for Diff-in-Diff approaches, it is pretty discipline-specific.
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Quoted from Forex.com.bd-Editorial External question — Cross Validated Stack Exchange Author: Stafanko Source score (net votes, not local likes): 3 Original post: https://stats.stackexchange.com/questions/381290 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I hope the group will be able to help on the following. I have the following policy-evaluation problem: stock exchange A reduced their trading costs after period X (say, after 2008), thus managing to spur their trading activity. Estimation of the actual effect is however impossible because of the lack of a counterfactual. Neighbouring exchange B, with similar characteristics and a similar trend in trading activity, does not apply any such or other confounding policies after the treatment period, thus representing a natural candidate as a control group. While this setup seems perfect for a difference-in-differences estimation, I am left with an excruciating doubt: in both the treatment and the control groups I have a sample of 1. Can one use the argument that if the series are stationary and ergodic, one can estimate a Conditional Average Treatment Effect on the Treated (that is, a treatment effect on the sample of the treated, in this case exchange A?). I haven't found similar applications, and perhaps for a reason... Waiting for your expert comments!
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