make_pliv_multiway_cluster_CKMS2021 {DoubleML} | R Documentation |
Generates data from a partially linear IV regression model with multiway cluster sample used in Chiang et al. (2021).
Description
Generates data from a partially linear IV regression model with multiway cluster sample used in Chiang et al. (2021). The data generating process is defined as
Z_{ij} = X_{ij}' \xi_0 + V_{ij},
D_{ij} = Z_{ij}' \pi_{10} + X_{ij}' \pi_{20} + v_{ij},
Y_{ij} = D_{ij} \theta + X_{ij}' \zeta_0 + \varepsilon_{ij},
with
X_{ij} = (1 - \omega_1^X - \omega_2^X) \alpha_{ij}^X
+ \omega_1^X \alpha_{i}^X + \omega_2^X \alpha_{j}^X,
\varepsilon_{ij} = (1 - \omega_1^\varepsilon - \omega_2^\varepsilon) \alpha_{ij}^\varepsilon
+ \omega_1^\varepsilon \alpha_{i}^\varepsilon + \omega_2^\varepsilon \alpha_{j}^\varepsilon,
v_{ij} = (1 - \omega_1^v - \omega_2^v) \alpha_{ij}^v
+ \omega_1^v \alpha_{i}^v + \omega_2^v \alpha_{j}^v,
V_{ij} = (1 - \omega_1^V - \omega_2^V) \alpha_{ij}^V
+ \omega_1^V \alpha_{i}^V + \omega_2^V \alpha_{j}^V,
and \alpha_{ij}^X, \alpha_{i}^X, \alpha_{j}^X \sim \mathcal{N}(0, \Sigma)
where \Sigma
is a p_x \times p_x
matrix with entries
\Sigma_{kj} = s_X^{|j-k|}
.
Further
\left(\begin{array}{c} \alpha_{ij}^\varepsilon \\ \alpha_{ij}^v \end{array}\right),
\left(\begin{array}{c} \alpha_{i}^\varepsilon \\ \alpha_{i}^v \end{array}\right),
\left(\begin{array}{c} \alpha_{j}^\varepsilon \\ \alpha_{j}^v \end{array}\right)
\sim \mathcal{N}\left(0, \left(\begin{array}{cc} 1 & s_{\varepsilon v} \\
s_{\varepsilon v} & 1 \end{array}\right) \right)
and \alpha_{ij}^V, \alpha_{i}^V, \alpha_{j}^V \sim \mathcal{N}(0, 1)
.
Usage
make_pliv_multiway_cluster_CKMS2021(
N = 25,
M = 25,
dim_X = 100,
theta = 1,
return_type = "DoubleMLClusterData",
...
)
Arguments
N |
( |
M |
( |
dim_X |
( |
theta |
( |
return_type |
( |
... |
Additional keyword arguments to set non-default values for the parameters
|
Value
A data object according to the choice of return_type
.
References
Chiang, H. D., Kato K., Ma, Y. and Sasaki, Y. (2021), Multiway Cluster Robust Double/Debiased Machine Learning, Journal of Business & Economic Statistics, doi:10.1080/07350015.2021.1895815, https://arxiv.org/abs/1909.03489.