generate_data: Generate a Dataset for Simulations

Description

Generates data according to the following process: \(Y_{it} = \alpha_{i} + \gamma_{t} + \theta_{1} Y_{i,t-1} + \theta_{2} D_{it} + \varepsilon_{it}\) and \(D_{it} = \rho D_{i,t-1} + v_{i,t}\). Note that \(D_{it}\) is predetermined with respect to \(\varepsilon_{it}\).

Usage

generate_data(
  N,
  P,
  sigma_alpha = 1,
  sigma_gamma = 1,
  sigma_eps.d = 1,
  sigma_eps.y = 1,
  cov_eps = 0.5,
  rho = 0.5,
  theta = c(0.8, 1),
  seed = 202304
)

Value

A list of two P x N matrices named Y (outcome/response variable) and D (policy variable/treatment).

Arguments

N: An integer specifying the number of individuals.
P: An integer specifying the number of time periods.
sigma_alpha: Standard deviation for the normal distribution from which the individual effect alpha is drawn; default is 1.
sigma_gamma: Standard deviation for the normal distribution from which the time effect gamma is drawn; default is 1.
sigma_eps.d: Standard deviation for the error term associated with the policy variable/treatment (D); default is 1.
sigma_eps.y: Standard deviation for the error term associated with the outcome/response variable (Y); default is 1.
cov_eps: Covariance between error terms of Y and D, default 0.5.
rho: Autocorrelation coefficient for D across time, default 0.5.
theta: Regression Coefficients for univariate AR(1) dynamic panal, default c(0.8, 1).
seed: Seed for random number generation, default 202304.

Examples

Run this code

# Generate data using default parameters
data1 <- generate_data(N = 300, P = 40)
str(data1)

data2 <- generate_data(N = 500, P = 20)
str(data2)

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