simGLM: Generate an artificial data set for some GLM's with two-way...

In amrei-stammann/alpaca: Fit GLM's with High-Dimensional k-Way Fixed Effects

Generate an artificial data set for some GLM's with two-way fixed effects

Description

Constructs an artificial data set with n cross-sectional units observed for t time periods for logit, poisson, or gamma models. The “true” linear predictor (\boldsymbol{\eta}) is generated as follows:

\eta_{it} = \mathbf{x}_{it}^{\prime} \boldsymbol{\beta} + \alpha_{i} + \gamma_{t} \, ,

where \mathbf{X} consists of three independent standard normally distributed regressors. Both parameter referring to the unobserved heterogeneity (\alpha_{i} and \gamma_{t}) are generated as iid. standard normal and the structural parameters are set to \boldsymbol{\beta} = [1, - 1, 1]^{\prime}.

Note: The poisson and gamma model are based on the logarithmic link function.

Usage

simGLM(n = NULL, t = NULL, seed = NULL, model = c("logit", "poisson", "gamma"))

Arguments

n	a strictly positive integer equal to the number of cross-sectional units.
t	a strictly positive integer equal to the number of time periods.
seed	a seed to ensure reproducibility.
model	a string equal to "logit", "poisson", or "gamma". Default is "logit".

Value

The function simGLM returns a data.frame with 6 variables.

See Also

feglm

amrei-stammann/alpaca documentation built on Sept. 26, 2024, 7:19 a.m.