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An instrumental variable will help with both the OVB and the measurement error given that it is uncorrelated with the error. Because what does this instrument do? If you have an endogenous variable $D …

Commonly people do not plot the coefficient of $Z_1$ against $X$ because $Z_1$ is just going to be a constant so this plot will not tell you much. If you want to show an association between your instr …

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 …

The dummy variable trap is concerned with cases where a set of dummy variables is so highly collinear with each other that OLS cannot identify the parameters of the model. That happens mainly if you i …

There is no direct rule but it will be more difficult to find a treatment effect if the two groups are very different in size. For example, if your outcome $Y$ is a normal random variable then the min …

As whuber pointed out, Angrist is just applying the formula for the OLS estimator to the above stated regression equation. From an interpretation point, regressing $$s_{ij} = \alpha + \beta \overline{ …

The question is: if you had data on year of labor market entry, why would you still use your constructed measure? Typically instrumental variables are a way around measurement error. In your case you …

That's a very generic question that could be answered by any basic econometrics book. …

The age variable will not cancel due to the within transformation. However, it will be a problem for the fixed effects estimator if you have another variable that has a constant change over time. For …

In recent years the structural approach to econometrics compared to reduced form econometrics has become more popular. … This branch of econometrics, as a branch of statistics, does not seem to have any introductory textbooks so far. …

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 …

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 …

Nonetheless this is an important question and one that is fairly underexplored (at least in the econometrics literature). …

The common setting among all of those cases is that you have 2 estimators one estimator is consistent but less efficient the other estimator is more efficient but potentially inconsistent. For ins …

The way you have described the problem, females are not the control but the treatment group. Then a positive $\widehat{\beta}_3$ would imply that women's earnings have increased more relative to men's …