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The method is generally implemented using [[Statistics/OrdinaryLeastSquares|OLS regression]]. This model can be re-expressed in the general linear model, and can therefore be fit by [[Statistics/OrdinaryLeastSquares|OLS regression]].
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Given indicators ''T'' where ''T,,t,,=1'' if time period ''t'' is the second one, and ''S'' where ''S,,i,,=1'' if observation ''i'' is in the treatment group, fit the following model: Given indicators ''T'' where ''T,,t,,=1'' if time period ''t'' is the second one, and ''S'' where ''S,,i,,=1'' if observation ''i'' is in the treatment group, the model is written:

Difference in Differences

Difference in differences is a quasi-experimental method.


Description

The method can be expressed as:

graph.png

where A, B, C, and D represent sample statistics; and where A and C are from treated samples. There is an assumption of parallel trends: absent treatment, AC should be parallel to BD as average change would have been equivalent.

The interpretation becomes:

  • B is the baseline average
  • D-B is the time trend
  • A-B is the difference between groups
  • (C-A)-(D-B) is the difference in differences


Implementation

This model can be re-expressed in the general linear model, and can therefore be fit by OLS regression.

Given indicators T where Tt=1 if time period t is the second one, and S where Si=1 if observation i is in the treatment group, the model is written:

Yit = β0 + β1Tt + β2Si + β3(TtSi) + ϵit

The coefficients can be interpreted as:

  • β0 is the baseline average

  • β1 is the time trend

  • β2 is the difference between groups

  • β3 is the difference in differences

Fixed effects for both units and time periods are generally included, leading to these being called two-way fixed effects models.

Alternatively, calculate the first differenced outcomes as Y'i = Yi2 - Yi1. Now fit the model:

Y'i = β1 + β3Si + (ϵi2 - ϵi1)

Once again, β3 is the difference in differences.


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Statistics/DifferenceInDifferences (last edited 2026-09-01 04:43:05 by DominicRicottone)