⢠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
- DGP1 - does not violate the assumptions under which the estimator works
- 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
â Feel free (not required) to illustrate with a DAG
⢠Run a Monte Carlo simulation. At each replication - Generate a random draw from the DGP
1 - Estimate the model
- Save the estimates
⢠Report summary statistics of parameter estimates - Bias
- RMSE
- 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.