This class was created by Brainscape user Christian Lundqvist. Visit their profile to learn more about the creator.

Decks in this class (12)

Lecture 1 (Introduction and causal effects)
Explain the heckman selection model,
Qualitatively explain what the la...,
Use the outcome framework and der...
7  cards
Lecture 2 (Matching and related methods)
When do we say that something is ...,
What do we mean with partial iden...,
What is unconfoundness
20  cards
Lecture 3 (OLS and asymptotic theory)
The analogy principle,
What are the ols1 and ols2 assump...,
What does the central limit theor...
9  cards
Lecture 4 (Regression fundamentals)
What is the conditional expectati...,
Derive beta in the bivariate case...,
What does beta_1 measure in the m...
10  cards
Lecture 5 (Instrumental variables (IV))
Explain qualitatively the basic i...,
What are the basic iv stages,
What are the identification we ne...
22  cards
Lecture 6 (IV with heterogeneous treatment effects)
With z_i the instrument status fo...,
What kind of individuals do we re...,
How do the instrument effect the ...
15  cards
Lecture 7 (MLE and limited dependent variables)
What are the advantages and disad...,
Set up the latent index model and...,
What is maximum likelihood
11  cards
Lecture 8 (Duration models)
Briefly summarize the main points...,
What are duration models,
What are the three main reasons t...
12  cards
Lecture 9 (Generalized Method of Moments (GMM))
Explain the concept of mm derive ...,
Explain the difference between mm...,
Derive the gmm 2sls estimator
7  cards
Lecture 10 (Panel data)
What is the benifit of using pane...,
What do we need for consistent es...,
What is the random effect estimator
16  cards
Lecture 11 (DiD basics)
Use potential outcome framework t...,
Rewrite this basic dd model in te...,
State the identifying assumptions...
13  cards
Lecture 12 (DiD extensions)
Summarize the problem with stagge...,
What are the real assumptions we ...,
What comparisons do we do in twfe...
7  cards

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Econometrics 1 - Phd

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