Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

PHS 2000 > Econometrics > Flashcards

Econometrics Flashcards

1
Q

What matters for the relevance criterion and what is a rule of thumb?

A

Significance level (not magnitude)

F statistic of at least 20

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

How can you test the exclusion restriction?

A

You cannot, but you can test specific violations

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Two steps of IV analysis

A

First stage: regress predictor on instrument

Reduced form: regress outcome on instrument

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

What do you do if you have covariates in your IV analysis?

A

Include the same covariates in the first stage and the reduced form

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

What technique can you use if you have multiple instruments?

A

Two-stage least squares: takes optimally weighted combination of instruments

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

What is the ITT in an encouragement design?

A

Causal effect of encouragement

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

What can bias the ITT in an encouragement design?

A
  • Randomization failure
  • Spillover
  • Attrition correlated with instrument
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

In an encouragement design, what can we still know if the exclusion restriction is violated?

A

ITT is still valid

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

What additional requirement (besides the usual IV requirements) do we have in an encouragement design?

A

Need successful randomization (no failure by chance, manipulation, or attrition)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q 
What is the LATE?
What analysis does it result from?
Who does it apply to?
What assumptions are required?
Why does this matter?
A
  • Local average treatment effect
  • IV estimator
  • Compliers
  • Independence and monotonicity
  • Whether the compliers are a population we care about determines whether we have an external validity problem
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

What is the IV assumption of independence?

A

The instrument is effectively randomly assigned – it’s not related to potential outcomes or potential take-up.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

What is the IV assumption of monotonicity?

A

There are no “defiers”

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Four ways to test the validity of an instrument

A
  • Regress potential confounders on instrument
  • Estimate IV causal effect with and without covariates
  • Check whether attrition is correlated with instrument
  • Falsification tests
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Assumptions of regression discontinuity

A
  • Correct functional form between running variable and outcome
  • Potential outcomes continuous at threshold with no manipulation and nothing other than treatment happening at threshold (identification assumption)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

What is the trade-off for a smaller bandwidth? What is an alternative approach?

A
  • Reduces the importance of functional form assumptions so less likely to find a fake discontinuity
  • People in region tend to be more similar
  • Less precise estimates
  • Weight obs by distance from cutoff
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Validity/robustness checks for RD

A
  • Check stability of estimates using different bandwidths
  • Show that covariates are balanced around cutoff
  • Look for bunching in running var near cutoff
  • Estimate false cutoff
  • Look for evidence along causal pathway
  • Look for effect in groups that shouldn’t be affected
  • Run model with and without covariates
17
Q

Main assumption of DID

A

Treatment and control groups would have developed in same way in absence of treatment

18
Q

Violations of DID assumptions

A
  • Invalid comparator; groups had different trends in pre period
  • Mean reversion
  • Other programs targeted same groups at same time
  • Migration causing overestimation of impact
  • Spillover causing underestimation of impact
19
Q

Tests to assess validity of DID

A
  • Placebo tests
  • Test for parallel pre-trends
  • Control for other time-group varying factors
  • Look for evidence of spillover
  • Look for evidence of migration in response to program
20
Q

Rationale for standard error corrections in DID

A
  • Treatment is at group level so residuals will be correlated, biasing SEs towards zero
  • With 30+ groups, can use basic clustering
  • With fewer groups, can use bootstrapping or permutation tests
21
Q

Main assumption of ITS

A
  • Trend in pre period would have predicted trend in post period if not for intervention
22
Q

Advantages of ITS

A
  • Only comparing units to themselves; no cross-sectional confounding
  • Confounding from slow-changing factors captured in time trend
23
Q

Disadvantages of ITS

A
  • Event of interest may coincide with other things

- Change in data collection at same time as policy change may be problematic

24
Q

Assumption of CITS

A

Trend deviations in control areas would have been same as trend deviations in treatment areas

25
Q

What should we remember when modeling counts for an ITS?

A

Possibility of over-dispersion in a Poisson model

26
Q

What may happen if you have uncorrected autocorrelation in an ITS?

A

Bias in standard errors

PHS 2000 (4 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions