Quando eu estimo uma diferença no modelo de diferenças com dois períodos, o modelo de regressão equivalente seria
uma.
- onde é um manequim que é igual a 1 se a observação é a partir do grupo de tratamento
- e é um manequim que é igual a 1, no período de tempo após o tratamento ocorreu
Assim, a equação assume os seguintes valores.
- Grupo controle, antes do tratamento:
- Grupo controle, após tratamento:
- Grupo de tratamento, antes do tratamento:
- Grupo de tratamento, após o tratamento:
Portanto, em um modelo de dois períodos, a diferença na estimativa de diferenças é .
Mas o que acontece com relação a se eu tiver mais de um período pré e pós-tratamento? Ainda uso um boneco que indica se um ano é anterior ou posterior ao tratamento?
Ou adiciono manequins de ano sem especificar se cada ano pertence ao período pré ou pós-tratamento? Como isso:
b.
Ou posso incluir ambos (por exemplo, )?
c.
Em conclusão, como especifico uma diferença no modelo de diferenças com vários períodos de tempo (a, b ou c)?
Respostas:
As was pointed out correctly in the comments your proposed solution c) does not work out due to collinearity with the time dummies and the dummy for the post-treatment period. However, a slight variant of this turns out to be a robustness check. Letγs0 and γs1 be two sets of dummy variables for each control unit s0 and each treated unit s1 , respectively, then interacting the dummies for the treated units with the time variable t and regressing
An example cited in Angrist and Pischke (2009) Mostly Harmless Econometrics is a labor market policy study by Besley and Burgess (2004). In their paper it happens that the inclusion of state-specific time trends kills the estimated treatment effect. Note though that for this robustness check you need more than 3 time periods.
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I would like to clarify something (and indirectly address a question in the comments). In particular, it concerns the use of unit-specific linear time trends. As a robustness check, it would appear you are only interacting dummies for treated units (i.e.,γ1s ) with a continuous time trend. However, it is actually the case that you are interacting a full set of unit/state dummies (unit/state fixed effects) with a linear time trend variable.
Angrist and Pischke (2009) recommend this approach on page 238 in Mostly Harmless Econometrics. Differences in notation can cause confusion. Reproducing specification 5.2.7:
whereγ0s is a state-specific intercept, in accordance with the s subscript used in their book. You can view γ1s as the state-specific trend coefficient multiplying the time trend variable, t . Different papers use different notation. For example, Wolfers (2006) replicates a model incorporating state-specific linear time trends. Reproducing model (1):
where the model includes state and year fixed effects (i.e., dummies for each state and year). The treatment variableDs,t is when state s adopts a unilateral divorce regime in period t . Notice this specification interacts state dummies with a linear time trend (i.e., Timet ). This is yet another representation of state-specific linear time trends in your model specification.
Unit-specific linear time trends is also addressed in another post (see below):
How to account for endogenous program placement?
In sum, you want to interact all unit (group) dummies with a continuous time trend variable.
Paper by Justin Wolfers is below for your reference:
https://users.nber.org/~jwolfers/papers/Divorce(AER).pdf
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