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At least in the social sciences you often have panel data that has large N and small T asymptotics, meaning that you observe each entity for a relatively short period of time. This is why applied work …

What is a difference in differences estimator Difference in differences (DiD) is a tool to estimate treatment effects comparing the pre- and post-treatment differences in the outcome of a treatment an …

Start from the structural model, $$y_i = \alpha + \beta X_i + \epsilon_i$$ where the explanatory variable of interest $X_i$ has a correlation with the error term, $Cov(X_i,\epsilon_i)\neq 0$. In this …

In your setting you already control for aggregate time effects via the inclusion of time dummies ($\text{month}_t$), which are more flexible than a linear time trend. To probe for the robustness of th …

There are two reasons for including covariates in a difference in differences regression: for identification of the treatment effect; to reduce the error variance (i.e. increase power of statistical …

A key assumption of difference-in-differences (DID) is that both groups have a common trend in the outcome variable before the treatment. This is important in order to make the argument that the chang …

For your first point, plotting the average of the outcome for the treatment and control group over time would be the right thing to do in order to see the unconditional evolution of the outcomes in bo …

The difference-in-differences equation you are running is $$ y_{it} = \alpha_i + \gamma \text{post}_t + \beta (treat_i \cdot post_t) + \epsilon_{it} $$ where $\alpha_i$ are the individual fixed effec …

You are right in saying that the IV coefficient is proportional to the reduced form and, in the case of $\widehat{\beta}_1$ it is the same. However, this is not a test for whether the exclusion restri …

In simulation studies the term median bias refers to the absolute value of the deviations of an estimator from its true value (which you know in this case because it is a simulation so you choose the …

What you are attempting to do is sometimes referred to as staggered difference-in-differences. So you have units which receive a treatment at different points in time. Your specification, $$ y_{it} = …

The treatment effect in your case is in percent and not in percentage points. To see this, consider the following simple example with two groups and two time periods for before and after the intervent …

There was a similar question a few days ago which had the relevant reference: Belloni, A., Chernozhukov, V., and Hansen, C. (2014) "Inference on Treatment Effects after Selection among High-Dimensio …

Both $r_t$ and $div_t$ are functions of $p_{t-1}$, hence the two variables will be correlated with the past price. However, $p_{t-1}$ does not occur in your regression and therefore is left in the err …

I think the problem is that the "parallel trends" (or parallel paths) assumption here was confused with a "parallel growth" assumption. Two lines are parallel over time when the distance between the t …