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Regression discontinuity

• Randomized experiments
• Estimation under unconfoundedness using matching and propensity score weighting
• Instrumental variables
• Difference-in-differences
• Synthetic control
• Regression discontinuity
This assignment is about exploring how the estimators perform under different data generating processes (DGPs). Specifically, pick two or three estimators and do the following for
each estimator:
• Generate data using two DGPs

  1. DGP1 - does not violate the assumptions under which the estimator works
  2. DGP2 - violates at least one of the assumptions
    • For each DGP, describe it and explain how it does/does not satisfy the requirements for
    identification of the parameters (and which parameters are you identifying?)
    • Also, give a real life example of a situation which might be consistent with this DGP
    – Feel free (not required) to illustrate with a DAG
    • Run a Monte Carlo simulation. At each replication
  3. Generate a random draw from the DGP
    1
  4. Estimate the model
  5. Save the estimates
    • Report summary statistics of parameter estimates
  6. Bias
  7. RMSE
  8. Size
    • Comment on the results. Are the estimates from DGP1 and DGP2 as expected?
    Turn in your code. The commentary can be in the form of a markup document or a separate
    pdf.

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