The estimate produced by the DiD estimator in this example is:

Question

BlackTom AI · Accepted Answer

The question asks for the estimate produced by the DiD estimator in the given example, and the provided answer is 'Positive'.

First, note the setup: there are two groups - Treated (cyan) and Not Treated (red) - observed across three time periods: Pre-Treatment, Treatment, and Post-Treatment. The DiD estimate is computed as: (post_treated - post_control) - (pre_treated - pre_control).

Step-by-step evaluation:
- Pre-Treatment values: Treated is around 10, Not Treated is around 9. The pre-treatment difference is roughly 10 - 9 = 1.
- Post-Treatment values: Treated is around 12, Not Treated is around -6. The post-treatment difference is roughly 12 - (-6) = 18.
- Difference-in-differences: 18 (post difference) minus 1 (pre difference) equals approximately 17.

Because the DiD estimate is a positive number (approximately 17 in this visualization), the result indicates a positive effect of the treatment as measured by the DiD framework.

If there were alternate answer choices, one would evaluate them similarly by comparing the changes across periods for treated vs. control and subtracting the baseline difference, but since no other options are provided in this prompt, the conclusion aligns with the given answer: the DiD estimate is positive.

In summary, the longer post-treatment gap in favor of the treated group (relative to the pre-treatment gap) yields a positive DiD estimate, reflecting a positive treatment effect in this example.

FA25_QTM_110_1 Quiz #8: Difference in Differences

The estimate produced by the DiD estimator in this example is:

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类似问题

位置6的问题 Difference-in-differences is only valid when treated and untreated units have identical values for observable pre-treatment features.TrueFalseDon't Know清除选择

Select all of the statements that are true about Difference-In-Differences research designs:

For these two units, the parallel trends assumption is met.

Simply comparing the treated unit to the untreated unit in the post-treatment period would underestimate the true treatment effect.

Assuming there are no unequal shocks post-treatment, the DiD estimator is unbiased for the true treatment effect for the treated unit.

The estimate produced by the DiD estimator for this example is:

For these two units, the parallel trends assumption is met.

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