make_plr_turrell2018: Generates data from a partially linear regression model used... In DoubleML: Double Machine Learning in R

 make_plr_turrell2018 R Documentation

Generates data from a partially linear regression model used in a blog article by Turrell (2018).

Description

Generates data from a partially linear regression model used in a blog article by Turrell (2018). The data generating process is defined as

d_i = m_0(x_i' b) + v_i,

y_i = \theta d_i + g_0(x_i' b) + u_i,

with v_i \sim \mathcal{N}(0,1), u_i \sim \mathcal{N}(0,1), and covariates x_i \sim \mathcal{N}(0, \Sigma), where \Sigma is a random symmetric, positive-definite matrix generated with clusterGeneration::genPositiveDefMat(). b is a vector with entries b_j=\frac{1}{j} and the nuisance functions are given by

m_0(x_i) = \frac{1}{2 \pi} \frac{\sinh(\gamma)}{\cosh(\gamma) - \cos(x_i-\nu)},

g_0(x_i) = \sin(x_i)^2.

Usage

make_plr_turrell2018(
n_obs = 100,
dim_x = 20,
theta = 0.5,
return_type = "DoubleMLData",
nu = 0,
gamma = 1
)


Arguments

 n_obs (integer(1)) The number of observations to simulate. dim_x (integer(1)) The number of covariates. theta (numeric(1)) The value of the causal parameter. return_type (character(1)) If "DoubleMLData", returns a DoubleMLData object. If "data.frame" returns a data.frame(). If "data.table" returns a data.table(). If "matrix" a named list() with entries X, y and d is returned. Every entry in the list is a matrix() object. Default is "DoubleMLData". nu (numeric(1)) The value of the parameter \nu. Default is 0. gamma (numeric(1)) The value of the parameter \gamma. Default is 1.

Value

A data object according to the choice of return_type.

References

Turrell, A. (2018), Econometrics in Python part I - Double machine learning, Markov Wanderer: A blog on economics, science, coding and data. https://aeturrell.com/blog/posts/econometrics-in-python-parti-ml/.

DoubleML documentation built on June 22, 2024, 10:50 a.m.