This repository contains code and knitted documents of simulations I worked on as part of Econ metrics classes at UO.
DGP: $y = \alpha+ \beta_x + \epsilon$
$\epsilon$ is described as following in different DGPs:
- Homoskedastic disturbance
- Heteroskedastic error
- Classical measurement error in
x
- Non-classical measurement error in
x: heteroskedastic noise
- Non-classical measurement error in
x: correlated with x
DGP: $Y_i = \alpha + \tau_{g(i)} D_i + \gamma Z_{3i} + w_i + u_i$
We have three possible instruments for $D$. Each is binary (with 50% chance of being equal to $1$) and affects a specific group.
-
$Z_1=1$ increases the probability of treatment for group $a$ from 0.1 to 0.5;
-
$Z_2=1$ increases the probability of treatment for group $b$ from 0.3 to 0.6;
-
$Z_3=1$ increases the probability of treatment for group $c$ from 0.2 to 0.8.
- For group $d$, the probability of treatment is 0.7.
DGP: $y = f(x_{1}, x_{2}, T),$ and $Z = 1 + a_1x_1 + a_2x_2$ determines the probability each individual gets treated, such that $$Pr[T=1] = \frac{1}{1+e^{-Z}}~.$$
DGP: $Y_C \sim \mathcal{N}(0, \mu_C); Y_T \sim \mathcal{N}(1, \mu_T); \mu_T > \mu_C$, and $y = f(T)$
DGP: $Y = \beta_1X + \beta_2Tr + \beta_3XTr + \beta_4T + beta_5TTr + \beta_6Cx + \epsilon_i + \epsilon_t + \epsilon_c + \epsilon_{it}$
DGP: $Y = \beta_1X + \beta_2Tr + \beta_3XTr + \epsilon_{it}$
$Y = \beta_1X + \beta_2Tr + \beta_3XTr + \epsilon_{it} + \epsilon_i$
$Y = \beta_1X + \beta_2Tr + \beta_3XTr + \epsilon_{it} + \epsilon_t$
$Y = \beta_1X + \beta_2Tr + \beta_3XTr + \epsilon_{it} + \epsilon_i + \epsilon_t$
Estimation: $y_{it} = \alpha + \sum_{t \ne T-1}\beta_t(T_{it}=1) + \lambda_{i} + \mu_{t} + e_{it}$
Violations:
- Non-parallel trends
- Parallel pre-trends and non-parallel post-trends
- Ashenfelter dip
- Anticipated treatment
- Unbalanced data
- Staggered treatment
- Heterogeneous treatment
DGP: $Y = \beta_1X + \beta_2Tr + \beta_3XTr + \epsilon_{it} + \epsilon_i + \epsilon_t$
Clustering Specifications:
