Quizzes Flashcards
(10 cards)
Fixed Effects: 1. Interpret the goals coefficient in the following regression
Estimate Std. Error t value Pr(>|t|)
goals 0.176069 0.034281 5.136076 1.3377e-05 ***
assists -0.063460 0.084675 -0.749447 4.5906e-01
wc_win 0.303101 0.109511 2.767761 9.3052e-03 **
For each additional goal scored, the Ballon d’Or points outcome increases by 18 percentage points for the same club, holding other controls fixed
Fixed Effects: 3. What does the bind_rows() command do in R, and why is it so useful
It stacks observations on top of each other, based on the variable/column names being
named the same. In contrast to rbind(), bind_rows() does not require the variables
in each dataset to be in the same order
Random Effects: 2. Interpret the goals coefficient in the following regression.
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.46846 0.11278 51.73985 13.021 < 2e-16 ***
goals 0.15776 0.04782 184.52401 3.299 0.00116 **
assists -0.03443 0.09951 182.88801 -0.346 0.72977
wc_win 0.31372 0.18040 183.94723 1.739 0.08370
For each additional goal scored, the Ballon d’Or points outcome increases by 15.7 % for
the same club, holding other controls fixed
Matching/Weighting: 1. Which causal inference assumption does common support refer to? How
would you show if something has common support?
Common support refers to the positivity assumption. You can show common support
through overlapping density plots in which one line is treatment and the other line is
control. If the two lines are overlapping, then there is common support.
Matching/Weighting: 2. Which causal inference assumption does balance refer to? How would
you show if something has balance?
Balance refers to the ignorability assumption. You can show balance through a balance
table, which depicts the difference between the treatment and control distributions of
each confounding 𝑍 variable. If p-values indicating the difference between these distributions are below .05, or if the standardized mean differences are above 0.1, then there
is little balance.
Matching/Weighting: 3. Should we use propensity scores for matching or weighting? Please pro-
vide a one-word answer
Weighting
Matching/Weighting: 4. What is the basic intuition behind the matching frontier? Explain in
words and draw the graph(s)
The idea is that there is trade-off between balance and sample size. As you decrease the
sample size, you increase balance—but only to a point. At that point, there isn’t much
utility from a balance/causal inference perspective from dropping more units. From an inference perspective, the idea is to estimate the effects for all points along the frontier to get a sense of the external validity of the estimiates. This way, we can see if the estimates to which samples are picked.
DiD: 1. When estimating any difference-in-differences using regression, what is
the coefficient of interest? Provide an example to show your knowledge.
The coefficient of interest is 𝛽(𝑇 𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡×𝑇 𝑖𝑚𝑒)
Take Card and Krueger (1994). In this study, the treatment is New Jersey (where the
minimum wage law got passed), and we are interested in the post-treatment period.
Thus, in Card and Krueger (1994), the main coefficient of interest is 𝛽(𝑁 𝐽 × 𝑃 𝑜𝑠𝑡 −
𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝑝𝑒𝑟𝑖𝑜𝑑)
DiD: 2. What does staggered adoption mean in the context of difference-in-
differences? Be specific and provide an example
Staggered adoption simply means that the exposure to the treatment takes place for
different units at different time periods. In Motolinia (2021), not all legislative districts
in Mexico adopted the lifting of the ban on reelection at the same time.
DiD: 3. Explain the difference-in-differences strategy in Helms (2024) in detail
Helms (2024) uses the end of the of the Multi-Fibre Agreement (MFA) in 2005 as
an exogenous shock/treatment. Then, he interacts that post-treament time dummy
variable with the district-level employment levels in the textile sector in 2004. The idea
is that having high-level of textile employment will increase riots in the post-treatment
period.
